{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table style=\"border: none\" align=\"center\">\n",
    "   <tr style=\"border: none\">\n",
    "      <th style=\"border: none\"><font face=\"verdana\" size=\"4\" color=\"black\"><b>  Demonstrate detection of adversarial samples using ART  </b></font></font></th>\n",
    "   </tr> \n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook we demonstrate the detection of adversarial samples using ART. Our classifier will be a **ResNet** architecture for the [CIFAR-10](https://www.cs.toronto.edu/~kriz/cifar.html) image data set.\n",
    "\n",
    "\n",
    "## Contents\n",
    "\n",
    "1.\t[Loading prereqs and data](#prereqs)\n",
    "2.  [Evaluating the classifier](#classifier)\n",
    "3.  [Training the detector](#train_detector)\n",
    "4.  [Evaluating the detector](#detector)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"prereqs\"></a>\n",
    "## 1. Loading prereqs and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from keras.models import load_model\n",
    "\n",
    "from art.config import ART_DATA_PATH\n",
    "from art.utils import load_dataset, get_file\n",
    "from art.classifiers import KerasClassifier\n",
    "from art.attacks import FastGradientMethod\n",
    "from art.detection import BinaryInputDetector\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the CIFAR10 data set and class descriptions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test), min_, max_ = load_dataset('cifar10')\n",
    "\n",
    "num_samples_train = 100\n",
    "num_samples_test = 100\n",
    "x_train = x_train[0:num_samples_train]\n",
    "y_train = y_train[0:num_samples_train]\n",
    "x_test = x_test[0:num_samples_test]\n",
    "y_test = y_test[0:num_samples_test]\n",
    "\n",
    "class_descr = ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"classifier\"></a>\n",
    "## 2. Evaluating the classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the pre-trained classifier (a ResNet architecture):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = get_file('cifar_resnet.h5',extract=False, path=ART_DATA_PATH,\n",
    "                url='https://www.dropbox.com/s/ta75pl4krya5djj/cifar_resnet.h5?dl=1')\n",
    "classifier_model = load_model(path)\n",
    "classifier = KerasClassifier(clip_values=(min_, max_), model=classifier_model, use_logits=False, \n",
    "                             preprocessing=(0.5, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 32, 32, 16)   448         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 32, 32, 16)   64          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 32, 32, 16)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 32, 32, 16)   2320        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 32, 32, 16)   64          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 32, 32, 16)   0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 32, 32, 16)   2320        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 32, 32, 16)   0           activation_1[0][0]               \n",
      "                                                                 conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 32, 32, 16)   64          add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 32, 32, 16)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 32, 32, 16)   2320        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 32, 32, 16)   64          conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 32, 32, 16)   0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 32, 32, 16)   2320        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 32, 32, 16)   0           add_1[0][0]                      \n",
      "                                                                 conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 32, 32, 16)   64          add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 32, 32, 16)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 32, 32, 16)   2320        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 32, 32, 16)   64          conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 32, 32, 16)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 32, 32, 16)   2320        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 32, 32, 16)   0           add_2[0][0]                      \n",
      "                                                                 conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 32, 32, 16)   64          add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 32, 32, 16)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 32, 32, 16)   2320        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 32, 32, 16)   64          conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 32, 32, 16)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 32, 32, 16)   2320        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 32, 32, 16)   0           add_3[0][0]                      \n",
      "                                                                 conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 32, 32, 16)   64          add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 32, 32, 16)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 32, 32, 16)   2320        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 32, 32, 16)   64          conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 32, 32, 16)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 32, 16)   2320        activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 32, 32, 16)   0           add_4[0][0]                      \n",
      "                                                                 conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 32, 32, 16)   64          add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 32, 32, 16)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 16, 16, 32)   4640        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 16, 16, 32)   128         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 16, 16, 32)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 16, 16, 32)   544         add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 16, 16, 32)   9248        activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 16, 16, 32)   0           conv2d_14[0][0]                  \n",
      "                                                                 conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 16, 16, 32)   128         add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 16, 16, 32)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 16, 16, 32)   9248        activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 16, 16, 32)   128         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 16, 16, 32)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 16, 16, 32)   9248        activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 16, 16, 32)   0           add_6[0][0]                      \n",
      "                                                                 conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 16, 16, 32)   128         add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 16, 16, 32)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 16, 16, 32)   9248        activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 16, 16, 32)   128         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 16, 16, 32)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 16, 16, 32)   9248        activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 16, 16, 32)   0           add_7[0][0]                      \n",
      "                                                                 conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 16, 16, 32)   128         add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 16, 16, 32)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 16, 16, 32)   9248        activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 16, 16, 32)   128         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 16, 16, 32)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 16, 16, 32)   9248        activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 16, 16, 32)   0           add_8[0][0]                      \n",
      "                                                                 conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 16, 16, 32)   128         add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 16, 16, 32)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 16, 16, 32)   9248        activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 16, 16, 32)   128         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 16, 16, 32)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 16, 16, 32)   9248        activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 16, 16, 32)   0           add_9[0][0]                      \n",
      "                                                                 conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 16, 16, 32)   128         add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 16, 16, 32)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 8, 8, 64)     18496       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 8, 8, 64)     256         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 8, 8, 64)     0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 8, 8, 64)     2112        add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 8, 8, 64)     36928       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 8, 8, 64)     0           conv2d_25[0][0]                  \n",
      "                                                                 conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 8, 8, 64)     256         add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 8, 8, 64)     0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 8, 8, 64)     36928       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 8, 8, 64)     256         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 8, 8, 64)     0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 8, 8, 64)     36928       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 8, 8, 64)     0           add_11[0][0]                     \n",
      "                                                                 conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 8, 8, 64)     256         add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 8, 8, 64)     0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 8, 8, 64)     36928       activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 8, 8, 64)     256         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 8, 8, 64)     0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 8, 8, 64)     36928       activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 8, 8, 64)     0           add_12[0][0]                     \n",
      "                                                                 conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 8, 8, 64)     256         add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 8, 8, 64)     0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 8, 8, 64)     36928       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 8, 8, 64)     256         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 8, 8, 64)     0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 8, 8, 64)     36928       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 8, 8, 64)     0           add_13[0][0]                     \n",
      "                                                                 conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 8, 8, 64)     256         add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 8, 8, 64)     0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 8, 8, 64)     36928       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 8, 8, 64)     256         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 8, 8, 64)     0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 8, 8, 64)     36928       activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 8, 8, 64)     0           add_14[0][0]                     \n",
      "                                                                 conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 8, 8, 64)     256         add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 8, 8, 64)     0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 8, 8, 64)     0           activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 1, 1, 64)     0           dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 64)           0           average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "classifier (Dense)              (None, 10)           650         flatten_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 470,218\n",
      "Trainable params: 467,946\n",
      "Non-trainable params: 2,272\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "classifier_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the classifier on the first 100 test images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original test data (first 100 images):\n",
      "Correctly classified: 98\n",
      "Incorrectly classified: 2\n"
     ]
    }
   ],
   "source": [
    "x_test_pred = np.argmax(classifier.predict(x_test[:100]), axis=1)\n",
    "nb_correct_pred = np.sum(x_test_pred == np.argmax(y_test[:100], axis=1))\n",
    "\n",
    "print(\"Original test data (first 100 images):\")\n",
    "print(\"Correctly classified: {}\".format(nb_correct_pred))\n",
    "print(\"Incorrectly classified: {}\".format(100-nb_correct_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For illustration purposes, look at the first 9 images. (In brackets: true labels.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(0, 9):\n",
    "    pred_label, true_label = class_descr[x_test_pred[i]], class_descr[np.argmax(y_test[i])]\n",
    "    plt.subplot(330 + 1 + i)\n",
    "    fig=plt.imshow(x_test[i])\n",
    "    fig.axes.get_xaxis().set_visible(False)\n",
    "    fig.axes.get_yaxis().set_visible(False)\n",
    "    fig.axes.text(0.5, -0.1, pred_label + \" (\" + true_label + \")\", fontsize=12, transform=fig.axes.transAxes, \n",
    "                  horizontalalignment='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate some adversarial samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "attacker = FastGradientMethod(classifier, eps=0.05)\n",
    "x_test_adv = attacker.generate(x_test[:100]) # this takes about two minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the classifier on 100 adversarial samples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial test data (first 100 images):\n",
      "Correctly classified: 20\n",
      "Incorrectly classified: 80\n"
     ]
    }
   ],
   "source": [
    "x_test_adv_pred = np.argmax(classifier.predict(x_test_adv), axis=1)\n",
    "nb_correct_adv_pred = np.sum(x_test_adv_pred == np.argmax(y_test[:100], axis=1))\n",
    "\n",
    "print(\"Adversarial test data (first 100 images):\")\n",
    "print(\"Correctly classified: {}\".format(nb_correct_adv_pred))\n",
    "print(\"Incorrectly classified: {}\".format(100-nb_correct_adv_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the adversarial images and their predicted labels (in brackets: true labels)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IwgtKv46uUUzdVCqDd4oy/zdTCh6NcW6OrSoPTmyzjzQXKKxLbQXRcJrnN83Tk6Pjo8hCK5zXJ7e4v4nPTSB7Sqn4HQuzuv2NyWlknb28z/71pz+LrLed99TTx96N7OEPfxDZWwd4jGKRD6gU8qxuXMrw/QY858tsgEL2ftgnOIZhGIZhNBy2wDEMwzAMo+GwBY5hGIZhGA2HLXAMwzAMw2g49pWM2zo65Yn/4SN12cf+jJVIWzv6kA29423ImoMUbHsC3N/cIqWwYJjCrg+wyvCh6mlkZ5dYEbG1k0JUauSUcgzKueEwxcva1EVkpSSrQgZCrL4cdBQWP3eOP3n/3VG+tnmDUl1LM0Ws+Sxlx8oaq1FuhCnVdW2x7ddDrO66GmVWyFJae6hGETwUYd84iLhaSEL5+r5T7aSwXg5QTq9FFWk0Qqm76tLINOE0wMsniQT/Zskoh/WKtKxVS66UKOI6z3PO1CgEdh5l38xdYv+SDvbXw61HkeUVydgtcn/JIN/IShPHjib2roSVasQc7jKjFBkOpPjahMSRLSkXRPGT5Y1Rx1gkX9qS56a+UpeVy5xfWzcoFFdnppDNpSiEx6IcT588/xlk/T2cw1tbVpENVtjgpQjvY6F1SryBDYrt4QKl6kKKfU66OJ5mnruB7MvpS8jyJT7YscMpV46efgiZMlVIdJjzukzzGr344llkhTyF7KXHH0PW10mheGeLbdo+sYysp/MYsq0cr+V+2Cc4hmEYhmE0HLbAMQzDMAyj4bAFjmEYhmEYDYctcAzDMAzDaDj2lYwDNS/N2/Xy58hjlP92WHRUHhmj7FsuU6aqKlUXu+OsdppeoNT0tsf/R2TDj30nsrGJKWSf+ORfIzvZSiHwRUV4DnlKZqPDlOCmFLNrS6niuH6ZQtnJcQpqWl3fkQEepHWCslyxRAk0HKIA7AZZCTetSJGlQYrR6y9QtMvMspLlUzs87uODbcgOIkvLy/Jbv/uxuiw5oIikOV6/I0dZibolxu0evv8ksqGUIgXXeO3n0pTJfUDRVTN8bV+CfeTkKUqRm618H16xZEcGR5ENnOQxvCwiyy1TOhxpH0CWKdGyjPbxGE3XKCdOVmaRba5TvJy5xSqr4njNT59mddzucWVyVGTuuGaBatb3AWRrNS9f/NNn6rKk8oDF3Bzl+c5OViP+9DO8JwSyzyALKX+edx1ntd+WGOfrqNK04RDPObRCiT0wNIZsW7mPhejEi1/kQyvFKO9t5Rrf3OGjJ7jDDPv6Ru4WsoDS6Qo9PMFqG6/H9z5BeTi/zntWocC2n3mWYvT2Ns9vYpYD4Eyc42limw8M7Yd9gmMYhmEYRsNhCxzDMAzDMBoOW+AYhmEYhtFw2ALHMAzDMIyGY1/J2EUCEhytrzI6/7Hnsd2HDr8L2cY2q7seaWVJ0HOzVGdDIxSiShXKVCMnKXvFm/m79asbFF3Hhlg9dW2Z8mssQiksMcf9zQQpgRZ2KFNFlhXRtJXnst3F7OhxyqcrlyhPDijSazJLkfNCgNmJLsrh2SAF0sEdro3XDlOC21ng9c1XWC0zvfjGWGsHg0Hp7KivoH3l8lVsFw5xaL3EoSPvHKXY2H/iOLLJAoXAwBzbdnSEVaLnlGvQH2d/TSuicJxdXQoljm0Jsr9Oz3G8O6dU+00qVZ8dz6Wm1GOdmKbEWEuzX68o0vLEBOXhajfnmdIOq/IWi5d5jC+wAm84xPno1FG237znewtk3hi1jLeLNTl7o/46XE+/jO3inm27NMU5suMk56CVMtuip4sPbFyfY/8adLwuW6usst3Xzz6XVSpbB79AOT05xIckovPsm9MzG8hWVijsahWwC9uc66c3p5C15nncUicfFmha5gMlPS0Usm/e5H1xJcf57fEdXo/UB9+P7JOf5rzQM8x58PNKdeNUQJH29+GNcVcxDMMwDMP4GrAFjmEYhmEYDYctcAzDMAzDaDhsgWMYhmEYRsOxr2RcCcRkKVb/k+WFAsW8qeIMssORtyBr3qCENB5j5dCVJcpomVAR2S/8199E9p2P/xiy4XZKy8srXNt1nGYF4EebKQU/e+lFZP1brAC50k8haiXGar/F86yCeajlI8gOj7OKdPMmqwxvXGfF6I12HrcvzMqwExOUWTvHKb0uz19H1t7Zj6wyTSEv2EPh7fnMOWQHkdauVnnXdz9al31k+9uxXUvTGjKnSLJNEQ7BLJ0+2V6nnFiLUYBvVWTajS1WSl1XqvN29paQLdV4fksLrCYaHqIoHMqxPwyHub/yPNul4ihBz8xzXugaYCXjQokSe2GHYu97jx5DFhukpH3lIi9IeuMryN71+LuRBQN8v1VPgdQ7zlFeOLYPIjVfls1KvcgeH1Cq7jZTYF1b5PxwKsRrVevidc463jualf4vA+yH0V7K38Uo99cRoKC83c0sVmH1/cICr1++wnE8Ns5+mJ7hHD64zPdx5MgoMq1q/cwOxeNHh04hO/p2ytyFEu/bk1/iffFL1/4G2Q9Gvg3Z0NBDyJ7+NO+Bvf3KQwUZ9pf9sE9wDMMwDMNoOGyBYxiGYRhGw2ELHMMwDMMwGg5b4BiGYRiG0XDsKxmLOHHBcF2yvUXhqGubVQiHhylAXn+ZZVELQYpdR4SS2UYnBcgbV3gu81UlUyq+Tlcpez0Y5vmlIhSdksOUc/M3KUmd2hlFNhOmLDexySqYiYdYZbJpg9U3y2lWhVzIUbSbm2eVzmKZUrU4SsbJHbbLilACHW+leHazl0JxV08nshZhm8olyqL3HB+WWrn+XBeFkn2LjCI7EmG/XoutINsJ8Dpv53lNpyamkE0sU24efpQC5KRSQfzFP6AkWA6Eka2uUBZtHhtFNt7Eyqsdecr4nR2U5+MPsm/23cdK3ocrHCeBLM85+Bi3m56cQhYKULI8eYJ9+KG3sA8neQhJpzl2bt1ifxk/RLk5mKQIexCJRKIyPHqkLuursh1XiwvI+svKXNrOqtilGiXswmKOJ9NHgTsQ43i6745q5CIipX5eq4tKpfh+z3tbuZ9C8U6e96LtEOf6y8pDAFLkAyo3I2y/sdA4siXPe2VKOZe+kxyLY0rl5q0CpepjJygo//4M+/X0rSlk8W6+t/WFP0PW0Xs/sp4A+8F+2Cc4hmEYhmE0HLbAMQzDMAyj4bAFjmEYhmEYDYctcAzDMAzDaDj2l4zLZZE7BN3BOIUjUSqgNq9SxJrJsZpu1yr3V+5eQhZbofwaCVGSyi1OIasVKYo90jyGrF+pDNvcThmtd+BhZMsrlMfWN24hC/TTRLz/fspUoTDPZVKpClzqZdXKnQ1KkZVJXqO+fr52corbXV3nOrinjVU1d9opd57soHgc3eZ26aJSvvcAspZblv/vv/5OXVYrUxwPCNuxdeQwsrZ29uHRI5RL+3qOIDudYPXg7vvYv1Y3eA2atijFX7yi9CXP95ZbZL8Onb2MbGmcY2x8mFL1OxSJ9/T4Y8jagpyu5rOUO0s1PhjQV2Il9O11Ctm9/TzG9jbnj84dXqOzL7Na7NISx3HTOB+s2L7E/t98mHPPQSTovXTu1PeTxTYKwAPdPchuhDnXry5fQbaxQRE3qLTtoFLtfbpGOTdWoSS+fZN9ZHCQfXi7wLHjZjh2ovEhZC0brCB+KM65dKHEh0IScVaH3lhjv+lRxuzgQw8ia15eRJYP86GVWI5jwns+ZPLooxSen7/K93tUqSCeqCq/XvDiC8iiJyk374d9gmMYhmEYRsNhCxzDMAzDMBoOW+AYhmEYhtFw2ALHMAzDMIyGY1/J2Dkn4VC9TNfRSjHvxFFKc65EIXbDs3rwhRgrE/b28rQSQYqp/auUqaZenEJ25mQHspEjFPgKy6z2+OcTlOVqwkquW62UoMthypMtG2yD8xlKcDWhLFrsoYh14yzFxu5eVoo8E6e09tIChc+eXgqpQ52sDprPU24b6RhFth6j8FnNUyIc6KekLaJUKr3H5Ld35IWXL9ZlY8OsFDxdvIYsvM7skbdTWJ9+geMpkuJ4Cp2icBdveiuy7RyF5/DqKF+boOxe6KR4+fYHTiM70sR+032CMuFD91GUbG/mnFKbpMg5qwiVi6sUgF9aYr9pb6PMfWWN/bBU5vsdjnA+ikQpn1Yr7OszM7xuhwsUYUuneS2PL2tj4uDhXECCdzwU0aFUnp9RqmcHeznnVqsUuIdCrG5ci3OOjETZZvf1sr03N9nnxo8eR5ZbYb8eP8LxPt/G+TVeVargKyLudp7v7a1veQuyWppjIneKY2ygSCF7e4X32fgYP99IT08hG0rxekxMcuz09LEq9XSWFdgv3/oMslg372MzU7zfDW18bQ+j2Cc4hmEYhmE0HLbAMQzDMAyj4bAFjmEYhmEYDYctcAzDMAzDaDj2l4zDIsFkvZzk+1kRMacIsTtdrDIpSlXI3PwUsiuOP29/PEhJsKP8ErLhPsrIS6sUz0bHTyJr7WClzQuf+j1ktyYpIvZ1Ku/XUexayrHy6oNdPOeulWeQtUcHkR1vZ1t9/sJ5nkuAl7qlhcJW5w63W1+l9DoaZrY0fQnZ6jbFwlPjlKC3V9kuB5FKuSi5O2S/ap7i+FsffgBZ1/2UCYdXksi+ePHTyJauUnb8gGMF1L+98JfIxo9Rniy1U879rg+yWnIgybHdsc2K5L2nOXaiK5Txvzx9A9n6FbZfy/p1ZJstfFhgLU9J9dK1q8gqing/HOa4i0QpaC4E+Xfgdn4K2YlOVsd94K2UvlfWm5GNKAL1jWVW+T2I1EIhKfXVP4gw0EXhNOD4fgb62WZLAfb1B/o5TlyO1285Rnk4EuX+Tt1HoX4xx4c97n+AD1hkg5zXT/exfy1mub+udt6LSjus5r+2xCrD8WFWLu9bp9w8+5UvIGsJ85y/XGOl5YEeXo9Jpcp8V08fsuoCr29oXfm1geu8J7ztLbwebT1sl2tTlPb3wz7BMQzDMAyj4bAFjmEYhmEYDYctcAzDMAzDaDhsgWMYhmEYRsPxmpWMI5F6eba9i9JVpcrdRHOskvvuMUpScy9SutpYYrXH1SQFzYEUpabLVyjnPvruf4Lsv3+ZVSHb2y8gK5cosgUdxa5LVymUbZUpaA4lWEF5ZYNiY2CN8vCNHLfrGmQF5YEHKFDPv0TxeCXH6xYLUiDtGGbF6Pwcz2WzQDG0f5jSWniTfWMsQSlR5JaS3VvK1arMbdYLui1Ztk/82w4j++EPfgDZ536fVT37O1l59YFm9oe1HPvhIaUibm2bIu67H6H8utpM8bhPON6Xo9yukqYQ+FLuBWQz57hdqcyKr7ExzgFbS+w3R+/nQw9jx1lpOZOhxB6OcGwPDr4X2eIix/bRNs5b7YpAGhykHHvjJvv1WUXQ7OqiVH0QCQSC0txcL5NGoxSpKy3s14EgH1bo7WN/yC2zIu6hY9xfi+P8uhjkPDfnlWrJCWYS4GcAXvlYYC4zh2ygj5Xsp27dRLahyORDiQSy1fMvIruxzqrdBc/2G9jkfB1a4PvdbuP4rIV4H9tQ3m9ceSAhNUih+IkjHLNnTj6G7KmJ55CVSpSbn5ZPIXsF+wTHMAzDMIyGwxY4hmEYhmE0HLbAMQzDMAyj4bAFjmEYhmEYDce+knEgEJCW1nq58WQvxUaXZcXSri5mxU1WNezspEhXLvEYxRrlya4Tb0O2nv4rZJm5NLL8dQqxlWoJmeJhycYGK6W29VAKa17nz73PLrCq5vuPUop8/hyP8dL6FLJ3PvEhZOEI3++EUvF1PczLv5Rh2x/tpDwZCVBaOzZ+CFmgm8fwObZzJcJ2OZCUiyJz9aJg9MFHsNkP/OAJZKUdVvs99MjjyMYDlASPhlkpu72VFYCDI0q18GWOxc08xdlUntvlVll5uNjF91FTBM3HOtivHxjkay9dvobs6Am230yawmIyzr/RwgFW/O7upxTZ1UUh9cZNvt+WY5x7trbZ/5+7Qgm66+zLyMrbk8iq/axKPTfLcXwQCQZD0t5Z35bRGKX4LqVK+k7uIrKbNykeD9TYX9drVWSdp+7j+SlVgZ2wv1aSHHfT07xW2yXOpYVu3semX5jgcft5zvEuCtl+jiJ6Zzel5ftHWAH48oVzyIYTfFhgu433rFKQVfq35zhfh8gcAAAgAElEQVTGSt3srztFzutdHcovEES5v3SFD/0MegrPPU/wYaOP/Q6ir2Kf4BiGYRiG0XDYAscwDMMwjIbDFjiGYRiGYTQctsAxDMMwDKPh2FcyFqlJrVJf2Xa7SMmnPcZKjNv9/Cn24OIMsuEhCoFPfY6iX+8pVrqtNlFCets7P4xs+ilWN36BvqJ89zseRtbcQZlwKcKKjaeSlDu/8AmKwp1Fil2RFspjx+5n+8ULSuVJRdKbnFak6gkKYE1RXstKhDJrh2cV2AeOUBattlPkDDtWWi7FWd14o0KZ7yASjrRLX/J9ddn3/fTfx3bbVVbtrhQoSm4WOHbGjh1BtiKUv1eq7P+Zlykn9vezmm42x2rEq0LBcHNjE1kw0IvsxS+cRdbdze3mJl9CNn6YQuq5v/gssskZzh8vD7Gdi2VWfC0V+d6amyl3XrzAauZxRQBeCLDtk0q2sUZhtjNGIXW1zDGWcNzfQaRaqcjKUn0/Lq3tYLtbq2zbdJXX5cRJVmLvTlCmnd7i3NyiSME9w+wjG0p172df5Gv7lWrEG8u8bUZa+H5bhlkpOxZhfxhUHvaY3OF761WqvW/kOH/03s/tmoOKeJ/j5xsVpbp3oZ8Py8zPKeK2cn7lMu876WXOPds3eK8MDfFeFFfex37YJziGYRiGYTQctsAxDMMwDKPhsAWOYRiGYRgNhy1wDMMwDMNoOPaVjKsVkc07fqX+0DDFwbXrrELo5ihsuUFWUyzfoqyaSFHYWrxEKWx5kBJeRyuP0XGaglpxmhWPy1UKTFeuUaZ6/HFKoEfGDiN7JrmO7OIlSmHtS5QdR4YpABc2KYuubFLOyi6zwm1bgVVqZ5RqrKeUCrKH+ykKD7cpkuoC18vdXRTPpmu8buVZRAeSru4e+fvf//31me/Ddn99kaJ3qURJtkdpi+klytrzSjXRwYQiD2doz/f38zrPC6u2BuhASyKh9JuzvFhLkTKyyiwruSoersx9gYJtTw/lzhXHqqjyEiXoTJiiZLfSwVY6Wcm4LcpscoJt2hzjMVaUhw/yIc4fzYVlZF0Jvta1vDGqe7uAk6ZYfT95Ps0HO5pbOM+9533fhqxYZrXf1DAf4picuIlsdZXVpMuL7F+3POXX5sM8xvYaRdczp5mtLXNeH+rj+KymOQfM9vFcyr0U2ydL7MNNFQrUgQX24eoAx3tZEdtDSun++QLvJ13dvCc89/wLyMKKtPzCBNtgVZHxxyvKGNvi2mA/7BMcwzAMwzAaDlvgGIZhGIbRcNgCxzAMwzCMhsMWOIZhGIZhNBz7SsbFzS2Z+OLTddkjj78X260GKEkN9LBabc6zImJsjCLd1nmKg72jlIyPJ59A9rkLv4lse51VYA+fosT1xTSFwKGHKXGNHXsLsqhS3fKxRx5BtrbCn7K/fIWVm5dXKanOrVHIvjbB91YojSLbaN1G9kCcUnU5QpFzs4lS3cUVioCJAM/v5ixFaz/Eaz5bowgrcvCqG5crecku1Mt05y/ksJ1LUJoLBinNDYXZtoshyo4J4WtvXKDoHYrQFJ6dZrXYsCLiRqI8l40t5X14vrZ9k9JhwFG+LgfZbwqe8mSJTqQsr7MvlZVKuJ3tnHsml9i/kmH2w8wC268/wrEdbGc14oU5ysPNEUqvteN8SCG3QLG8pZ1S7kHEey/FSr09fvKDH8J2oUXOr7lVXtNkgtXezy2yUvZoD+fmcoH9tTbLh2AeOsMHT1ZiFOqDZQrmmQXexwb6mC2c5TmvrSpz6bOcP3Y6eb97byfn5t5OHpd3BBEJOESTFb62O89777bj+Jy+xDFRqvJBg1sXriBrbmK/7uvjvHD9Bu+LuQuUuffDPsExDMMwDKPhsAWOYRiGYRgNhy1wDMMwDMNoOGyBYxiGYRhGw7GvZFypOVnI11c2zKy0YbsBraLqDqXD4ByFrZZtVuxtaqJIdPrUGWQ/9P2PIjuTO47s4//9V5A5x4qNHR0nkaWS3K5VkRiDJUph3dtryN4yxmOsN1EWffkcxa6XNqmPxYeP8Vx2KH3fN/4QssUQJcvNeR6jyT/A1y5QChsJUmTbKbAfbNOpk9l1Sn8iLynZvWVrc0O+9IXP1WWZGxTpJEiRbvjwUWQ3hI0R9EPI/ibDv0UCYfbNoRFeg1j0K8wU2TESo/CZa6Y4PhbhGIukKB6HAuzD2VX2r6Rj9fFLRQqa3Url4XKZff3ylYvIEo7W8lmlqmwiRfH+wiLF44485eGOFk6nRw4dQhYNs5xzoUBBM9nPas4HES8iZV/fvmMx9q+Q0haZecrV3rF9gotK/+cwkdgqReEdDgnZuXod2VqKcvNQjHN4IMxzye9Q2L125TKyZ6coxVeq7MPec35df/QdyLqPs19v3bqF7OUlPmTSpAjZU6sU5ZXbiaSryryuHLe5pQXZzDzXAcej7BtBRfi/nuVDOvthn+AYhmEYhtFw2ALHMAzDMIyGwxY4hmEYhmE0HLbAMQzDMAyj4dhXMt4sVOWpq/WS1dhbWRHUr/InzAN5SpZ+mJUYF56ZQpYUSq3vepTVg2WNguaDfayemv+e/w3Zn/zKbyBb2qFkmVmnVH2rQDFuOUOhMtrJc7l59WUeo0TBsNBLefjkA4eR1YSSWSFLSbvWOoCsnKVQ1jt6GtmLaQqkY0rVz4shCqR5x/fWu8T9tQ5TID2IhEI1GeirF8ozE3yPUqWcmC8qlYyTI8g2lArFxa2byDp6KQBfPzuFrLWd115ja4P9pjvBsXhtSZG/HaeSjqEUsuYIxcZza+eR9fdy7OQy7K+ZFYqIyRE+9LAQYdtrVWVFqbY+eISVpR9O8Jo3xyipTk1SZu0eo8g8NMjj7uTZhw4i1WpVtjbrzzWdZvXgI+Os4BykJy8LC4qI7ikeL5xjP7y5wTm8u5MPhVRvsGJv+hyP2zo+iqzvgV6+do7CeniIlZY7VpWKzGOcA4Is+iyFIiu7X31yAtlWO+9ZhUVKwedmLyGred5P8ptsq50d3vM7Onh/v36d81Y0xLlidpH3z7U1zkfVmlLifB/sExzDMAzDMBoOW+AYhmEYhtFw2ALHMAzDMIyGwxY4hmEYhmE0HPtKxoWqyPW1+jXQc7cuYLszI5Su/M4GsuYwS08m4qxiKr2UlUabHuZ2npLxlRtXkf3WZyijvTTD1xYVebiP7p9k5ik2eqUaZf8oReF0G8tqDolS7VSptHxtgdJhbIhio4QogG2WuL+NMs+lI0AJNFhTqo128bh9wsqrPS2Uh+cUCbpHOZcDiQ+KL3fURUdbKFxfVyo491Yp6B9uOYVs/j62YypMUfLcRY7FVIpjbG6OcmJ7OwXl1lZWFd/ITSH7wAf/HrIXNyjTrlzLIHMlyqJ5oYy8UeX8sbJEybglRnm4tMGx3aGIph98ghWZx1MUsvtXaHxuLfH6XlrnAwSDI3xv64rMHR9ilWsX2Hd6PjCUigWZuUOmboo8ge22N1j9eWeLc1pqkH14s0Kp+wvPs0J32zr3NxVmO4ajFMdDitk7V+Fra7Mcn8F+3sdKQfbN/n72r3CY200UbyArVHiMgDJtZq/xHugD/Cyjo4dz8/oa289XKfa2NXOumJ6YQlapsiJzexOrG19Z5zn39/cjy6TTyPbDPsExDMMwDKPhsAWOYRiGYRgNhy1wDMMwDMNoOGyBYxiGYRhGw7GvxRYMR6QtVV9l8WMvsarhF26xmuIHHzqJ7HCVP5M+ucMKht83yGq6Elaq85Z4+h8/Rwkpe5nH3Z6l8JZIsAJqdolCscQoBAYc30cgqqwfa5SuijVuN6PIVMmhUWQhRYKer1AK0ySzYJBZczOF2ROHWJEzPUeBtKpUsy1VKZX2xdnOS02Umw8iO+W8XJx/vi6rbvAidPZTvM+mWe20WxERyzEKtmud28gOKULspauU9XQo7G5pnncn+8j4w5QsP3Li3yKbefYZHnVNqdLcTeFZ5jhmQwFK+00B9vXesVFknS0UG6vCseguPIvs2pIiSyuVvCP9irjdw6qyUqDw361UpS5XlQcIDiBR52QsVN9PSjuUZOfKbAtXZVsERKlavERx/NgxVkaeVirsPtTCqvqLShXrw7FRZKtlzv9b659Htj7J+8l6XqmWPM8+NzzCytblMit5LwbYVm6B46Szm+Pz/MpFZBvpPLKgcoxCO9t0Y5KSfc8Oj1vp5ftNz7LKtXbvTad5T006zkfsaX+HfYJjGIZhGEbDYQscwzAMwzAaDlvgGIZhGIbRcNgCxzAMwzCMhmNfyTjkRXrK9QJldpU/R5+pUYD88jnKjtUyfxY+ociOfQ9SdJIgpbDn5ihO/dXfsrrl1hzPOTHE/S0sUIBMDvOcp6coScUHWGVytkL5NO4pn87Ms1LwcIjVPLOLbJfEIGXHcJTCbm6Z7+3YiRPIgguU6gJxnrNE2X4DCQqzlSpF2HKR/WW0KYaMtXHvPc3hJjmZqJfMX5ijEF4psvLqllCUn5xme3crw3JRKfidr/G6lMusMNouRb6Ybq4InUhp9RQ+X3qaFZSfaKHw+QEW8ZWdOAX9WkWRfdv5Pgolio1Xq3xvixc5nko5tvOFTsrchSWa1oce4PkVhJWRDx07jCzYxPHZHOXYjjZTgu7bfmNUMhYJSyBQL8qWlcrDSUeZdq7CsTN1nSO/quyvvYMV72NLHGM7QoE7Mk8598lt6qoDfZR9FzwHj1aNeDHHsROOKVI1u4iUqhRsty5zvHd18X3Mv3ATWV+CwnpAmetnSmzn1jFW2t/p4hwujlWQ56Y5FpMpCsVlZQ6tinJvG1aehOAS4qvYJziGYRiGYTQctsAxDMMwDKPhsAWOYRiGYRgNhy1wDMMwDMNoOPa32JxIKFgvRQ2HKZfOZikDFWuU5r5w8wqy7/swpcO1TgpRiXYKW3/47AvICp5ib28f5axlRbpNKZVht7cpUw0o4mVAqeI7qPhQ6XlWRR0doZzoFri/UIDVPJdW+drxdgqQoRD3N5PmuRQKrFpZXd9C1t3HtqoK27kmlNY6OjqQ7bRSKj2IRGIRGTpSL4leu3mNG1YpHba1UYrMblJ0FWEf3shyqx5FHi60UYhdU+RhTTLubGWl2arSh29+5hPIZq+zSvP9Kcrufp4HripVtrcCHGMve0rGNxVhvdrHiqrbzez/Oy2cZwZO86GCvk5eNwlwf5lFSpsd3azK3n2G7RxQ5tV5p1RRP4AUqztyfa1ePB/apEj9+flP8bVdvFb9s+zXM2U+2NHbSwHYKwLw9o3LyJbCHCdDQ7Tis47ya2hJEYWV+b/muV1XvoQst8jBPVPmPBz3fG2pzGxRGU9eqTxf2uF46qtwDl+8yod0djp5fhGlSn9CkZvnlIdqONpFhoY4h7oar7nILSXbxT7BMQzDMAyj4bAFjmEYhmEYDYctcAzDMAzDaDhsgWMYhmEYRsOxv2QcCkutr78uyqyxWuHAIKvQTqcpTx6igyQvXaMU9uA2y7a2etqOtVXak9FNZkuK1Nqj/KR8Lszm6FCySUWmGlbk4UCKlTuHQ5QJvVKlWRMMR5UKxVtlnst6JY+sqYmvLcbZzsUCxbP2giLGrVPujLTxWib6qY+dT7PKdXi1H9lBpFarSb5QL0Hef+YBbPf0k8/e1f7aFbk6u0WpW6PQymtfVQR4jTbhcde2OHa07ZS61tKujJOFdT5oUAxNIqsIr/2nMpRKbw7xvbUXKCK2KOOp0sPqqalxjoloCwXgtLBarJ+nQDp+hA9MdHRzu+LiIrJgKx8MSDUrFd0PIr4mrlTfZ5+b+xw26+/ndXbLbNuXV1iNODnI67eV53bLS3zg5XAzpf1kkvubm1Pk1xr7XFsvR0CpxGM45Z6w4jhma1k+iDEyyodHqso5r9zgXF/Y5gM0fT08Z6/cE+YzShVw5b31tVD2nVeqQzvH+7bW9gHlYYZFYft1bGtPTLw69gmOYRiGYRgNhy1wDMMwDMNoOGyBYxiGYRhGw2ELHMMwDMMwGo79JWMRkVq9OJqIKxVsy5SQBlKUs9IZVoUMLFIa+q2PfxLZe59glcnJeZ5+ezdlqtYq5afsKtd2Y538WfiqUp30WKdWiZfVTmdm+JP33vG4YaWqbDBIQblcpom1GGQVzIEB7nDCU1yt5ZkNBik25gqsLHomzuuxtryC7OYGz7k0QwF9ONaD7DqSe4+LBqVprF6end2ZwnbdPewjK8usCKr9idHWQdFVhOOkus521BS8tm+gSrRXTnCrjfu7WqJ03hGhxHi1wP19bpZC9soxjsXuIT7M0B6lsDic4AME0XGec6DG184rVVaDUYqrg2OjyHIRbtep2JNVRdrMOo7j8SSl5YOIk6IEaxN1WaDK+T87y4qzLsBrIBHOpXOzlNMVf1WSSc5LG4uUkWtry8gCVUrQgQT7640bN5ANR1gBezHE1w4pD3akpyeQNS+zav39I2PIst2cmzOznNd7lOr2i1ne28rdfKCkz/OerwnFSeWCBJSqysplk9WcIuNvcMzeaqFAvR/2CY5hGIZhGA2HLXAMwzAMw2g4bIFjGIZhGEbDYQscwzAMwzAajn0l41AwKN2d9XJSoUCN8eYOxaToIisex+MUytILU8gqz1GSmlSkpuBNVoC8tEVhq8LTk/EWCkxb26wKyZqoIqERyoQxpXrkseOUBENLlNuqLZQxZ9f4fkM1ysODirQ2XZpB1lOmxLsWowhYLbGx3vkIheeSp8xXXKcWPHGRVVszOUp/0sf3cRDxPiTlan0Vz/YdjolbO4pQHKfoF1EqpS4tUh6+Wzo6KSivr91d9c/2DsqdG4rIHNrkdn+8yerUz8RYyXglQsUw9PjDyOIpVkod6+MDBKe32V8DilCcF/avySzH2NDwEDKtDTa2mI32cIytrrEfHD1xCllbG9v0jYL3IalV669NPM65qlKhXC2O2fwir0u1yr/Fk4kEspmpKWQp5cGJxTDn8HCUD0kEmzjPxcYo+2qfFHjPqsXpEqXgaifnBU0Knpi4yWOUlIdHlHZ2GVYF7urs4v4qvG7pBYr3caXtRRGK/QJF5pyShfo5djYL3E7yO8z2wT7BMQzDMAyj4bAFjmEYhmEYDYctcAzDMAzDaDhsgWMYhmEYRsOxr2RcKxalOFkv7a5sUBAdilGSXU0oVSE95af4ICWuhVmKwn0Byk8LFQqsfb2UCStlipyFKPXhm+fOIUsFeH4rS2yDlkgY2aEmpXLzyCiyyCpf+0A/q7FqP1t/IUsBbKibgmZmiZJZdxclszNveQhZ3HMdfCVPiTykXN+2dW7X2c33tpO7gOwg4ktFKaen6rLFZfbNrj6OiV5FAJ6dYd9s6qA42DvAaqK+qowJdgcZGaN0m82ycm5XL8+v4ClF9oQo9rYfOoasuM3rfKiD88LJoxRsN/OcmlrzlEVXDrFduiocT9MZivfxMLdbXGIF2TGlgmxMEY+XlvkAwfgRPmiwpByjUxE+5zOUbQ8ipVJJJqen67KhIcra80ql28EQr/OwIgAHgtxfIMZ5qXlsHNn6IvvN6CBfm1Uk2bjyEMD8LCvUl2oUircrzAa7+N5mbvDhjG1lbu575B3Izj9P6TblON6LnXwfmbm7618Dyr3XzzOb9WyXkDLGDg3yoZXLyviUIh/6Eac8vLEP9gmOYRiGYRgNhy1wDMMwDMNoOGyBYxiGYRhGw2ELHMMwDMMwGo59JWMfCkrxDvktmqdIt56jwJrop5xbXeR2c0HKT+KZ1YSiWKlE0WktTZGtosiY0VFmA4rImaEPKNurMe6vl7LXyxkKVscLrHic72LFxqPSgaw6QOFzKEZpU/sp+1Sc4nG1nxJcLksxutzBc6l0UCC98UX2jWyZQurh0WFkhWZKufIio3tNJOJkcLi+j4VDR7BdpbCNLD3N6/LgQ+xzlSrb1gv79cICO+fIIf7NkkqygnhTK2XH1BDHbMdpRRIMURLcUir7nhmhGNo6yuu8sUwR/eQx9pGSIspLhBVQr6cpO/YrDzNEIzzuSJjnrMnDw6MUj906K0Z7z3mmp4cVmctlVmX3SuXyg0jERWQ4Wt8egSVF7E2xby4oVW3jIfbNrHA7N89quoGkIsQqlX3npznP1RShuKyIwl3KtcorFeCbC7wn5CbYR8JKgefLiij/kNK/+ko8l6kCzyVV5fwxEOfck1VEcBG2S1q5R8eF+2tX5vWb1y4jK4nSCJ5CcY/ykQxH599hn+AYhmEYhtFw2ALHMAzDMIyGwxY4hmEYhmE0HLbAMQzDMAyj4XCaBPfVf3QuJyLTr7qBYXxzGfHe046+h9iYMO4xNiYMo55XHRP7LnAMwzAMwzDeiNhXVIZhGIZhNBy2wDEMwzAMo+GwBY5hGIZhGA2HLXAMwzAMw2g4bIFjGIZhGEbD8aZb4DjnjjnnzjrnNp1z/+JbdMxfcM79r9+E/f64c+4/vt77NYxvFOfck865f/4q/zbsnNtyzgVfa9u7PNbTzrkHX2ObUeecd86pv7/nnPs/nHO/cZfHs3HXgBzke4Nzbso59/5X+bd3Oeeu3eXx7nfOfflrPc83Kvv+2GaD8tMi8nnv/ZlvxcGcc30i8o9FZPwb3M8TIvIx7/3tv3T46yJy0zn3i977xW9k/0bj4Zx7Unb7zF3duL9VeO9nRKT19diXc+47RWTTe//yN3hOP/81bG7jrjF5Q94bvPdfFJFjd7nteefcmnPuO733f/mNHPeNwJvuExwRGRGRS6/2j6/8Vfk68k9E5JPee/607DeI974gIp+S3UFiGG9GflREfu9beUAbdw1Lw9wbXoPfF5Ef+RYf857wplrgOOf+VkTeIyK/vPcR+VHn3G875/5f59wnnXN5EXmPc67DOfe7zrmcc27aOfevnXOBvX0EnXO/6Jxbcs5NOud+bL+PvkXkQyLy1B3n8ZG9j0I3nHO3nHMf3Mt/2Dl3Ze8j0gnn3I/s5S2yO6Em9857yzmX3NvdkyLy4de7rYyDgXPuX+71kU3n3GXn3N+77d/+nXPuY7f991e/hnHO/ZyIvEv+rq//8t42jzrnnnfOre/930dve/2Tzrl/75z78t5r/tI51+Oc+/29vvq8c270tu1fdV97HHbOPbf32j93znXfeZ6v8p7/6d44WHXOfdo5N/Iq20VE5L1y2/hyzr3NOffC3jEXnHP/+Y6X/YBzbmZv/P4rrS1vO7//yTk375zLOOd+6o79PCk27hqGg3BvcM71Ouc+sfcJy4pz7ouv7HuPM86583vj7Y+cc7G91z3hnEvftp8p59zP7M0Xq865j76y7R5Pisj7nHPR16XxDjLe+zfV/2T34v7z2/77t0VkXUQek90FX0xEfldE/lxE2kRkVESui8g/29v+R0XksogMikiXiHxORLyIhF7leDkRefi2/37b3vE+sHe8lIgc3/u3D4vIYRFxIvJuEdkWkbfs/dsTIpJW9v8WEVm51+1q//um9dd/ICLJvb7yfSKSF5HE3r/9O9n9CuqVbUdv74tKX+8WkVUR+SHZ/Xr6H+39d89t29/c64Mde/38uoi8f2/73xWRj34N+5oTkdMi0iIif/rKue53niLykb1zOLG3338tIl9+lbY5JSL5O7KviMgP7f3/rSLy9juO+esi0iQiD4hIUURO3NmWt237B3vnft/eOH7/bcexcddg/1PGy2/Lt/be8Asi8qsiEt7737vk735tYEpEnpPduaBbRK6IyI/u/dsTctu9YW/biyIytLft0yLy7+849oaI3H+v2/yb/b831Sc4+/Dn3vunvfc1ESmLyD8UkZ/x3m9676dE5BdldyIXEfleEfkl733ae78qIv/hNfbdKSKbt/33PxOR3/Lef9Z7X/Pez3nvr4qIeO//ynt/y+/ylIh8RnY7+X5syu7NyGhAvPd/7L2f3+srfyQiN2R3kfz18GERueG9/z3vfcV7/wciclVEvvO2bT661wfXZfdTw1ve+8957ysi8sci8uDXsK/f895f9N7nReTfiMj3utf+mP9HReQXvPdX9o7587L7l6v2Kc6dY0tkd/yOO+d6vfdb3vtn7vj3n/Xe73jvz4nIOdld6LwaP+u9z3vvL4jIR2V3EfcKNu7eHHwr7w1lEUnI7m8rlb33X/R7q5E9/sveXLAiIn8pIvu5Qr/svZ/d2/bnpL7vyt5xO1/j/N7w2AJnl9nb/v9e2V093/7jcdOy+0mLyO4K+vbtb///NVZld7X/CkMickvb0Dn3IefcM3sfT66JyLfvnc9+tMnuXxlGA+Kc+8d7X2eu7fWJ0/LafeLVSAp/FPH2vi0isnDb/7+j/PcrcvDd7Gv2jn8Ly2uf+4iI/NJt73dFdj/RTCnb3jm2RHb/gDgqIlf3vjb7jjv+PXvb/78t+8vOd55/8rb/tnH35uBbeW/4v2T308vP7CkK//KO7V+vvit7x117jfN7w2MLnF1uXyUvye5K+va/GIdl9+N2EZGM7H4E+QpDr7Hv87I74b7CrOx+BVDH3vehfyoi/0lEBrz3nSLySdmd3O88x9s5Ibt/iRoNxt6nFr8uIj8mu1/9dMruR8+v9Im8iDTf9pL4Hbu4s8/MS32/Fqnv218Ld7OvoTv+rSy742s/ZkXkR7z3nbf9r8l7rz3aelNEnHPuq4sf7/0N7/0/EpF+EfmPIvInew7b18Od5z9/23/buHtz8C27N+x9KvST3vtDIvJdIvITzrn3fZ3n/ap9d2+8RETkrh4tfyNjC5w78N5XReTjIvJzzrm2vZvMT4jIKzLnx0Xkf3HOpZxznSLyv7/GLj8puz7NK/ymiPywc+59zrnA3n6Oy26Hi8ru97IV59yHROTbbnvdgoj0OOfu/Fj83bL7VYLReLTI7gSbE9mV0GX3E5xXOCsij7vdujIdIvIzd7x+QUQO3fbfnxSRo86573e7IvL3ichJEfnE13Fud7OvH3TOnXTONYvI/ykif7I3vvbjV0XkZ9ut+vAAACAASURBVJxzp0RE9qTOf6Bt6L0vya7n8NXx5Zz7Qedc395XCq/8hVr7Ot6fiMi/cc41753LD4vIH932bzbu3mR8s+8NzrnvcM6NO+ec7H46WJWvv+/+z865Qbcr9v8rYd/9W+998evc9xsGW+Do/Ljs/nU8ISJfEpH/JiK/tfdvvy67bsx5EXlZdjtpRXY7o8bvisi3O+eaRES898/J7mT5f8tuJ35Kdr9z3RSRfyG7g2RVRL5fRP7ilZ3seTp/ICITex/fJ/fM+G8Xkd95fd62cZDw3l+W3e/4vyK7i5X7ZFcYfOXfPyu7E9d5EXlRuFD5JRH5nr0nKf6L935ZRL5DRH5SRJZlt+7Hd3jvX+tTFe3c7mZfvye7omZWdgXN1yye5r3/M9n95OUPnXMbsvuJ1Yf2ecmvyd85ECIiHxSRS865Ldl9///Qf/2P4T4lu58S/Y2I/Cfv/WdERGzcvan5pt0bROSI7C7Yt2R3zP+K9/7zX+d5/re9c5mQXSXi39/2bz8gu39INDyvGNrG18neJy2/6r1XH2Xd2+bnRWTRe///vM7H/nERGfLe//TruV/DeCPhnHtaRH7Mf4PF/m7b36iITIpIeE90vvPfbdwZr8m9ujc456Zk92mwzyn/dr+I/Jr3/h2v1/EOMrbA+RrZW22/R3ZXxwOy6808471/3X+KwTCMbz2vtcAxDI2Dcm/Yb4HzZsO+ovracSLys7L7NdLLsluP4N/e0zMyDMMw7jV2bzhg2Cc4hmEYhmE0HPYJjmEYhmEYDYctcAzDMAzDaDhsgWMYhmEYRsNhCxzDMAzDMBoOW+AYhmEYhtFw2ALHMAzDMIyGwxY4hmEYhmE0HLbAMQzDMAyj4bAFjmEYhmEYDYctcAzDMAzDaDhsgWMYhmEYRsMR2u8fW1pbfHdXV10Wa45hu2rZIQsEuXYKBsPIajX+WG+5XEYWFv5mVlmU47oIsmiE26nHCPP8tO28ci7qa73y2jJfG4nwnF1Z+RHjCI8hyvmphHmp9ZfeXbtUKjy/gFPWy2G+N1FeG6zsILsyMb3kve/TzvJeEYoGfbS5vj1cIIjtXIHvccdVkflaDVksxDHmQkr/Z7eWsNL/S4USX7td4GuDSj+M8hgB5bjeK9dZ+Zm7WLPSRxzbQBsTpRLfR0Tpc9UqD1xROntJOb+uznZkXjnu8gbbr6WF5xxQ+nq+xHPR+lA0xsZfX1o/cGOirSni+9qa6jL2dJGVnSKyYJXXrznKDtakzEFS5AX0YWWcKMdwjtcgEOB2lQrfSUSZh3dq7CPVIvt1WJvrA5ybC0XuT5s3pcb3G3TK/U54zl6Z64MhnkslwOOqP2Hp+dqA0vbhILcrVpRzCSrXUhnHW6Xyq46JfRc43V1d8hM/+eN12ZH7jmK7tUVetPa2VmSt3f3I8hvLyBYW5pANKLNlTumQTaFRZGODfJuZhXlkiWSC281nkFVr7PSJOF87V0sjq2X4PoaGhpAFFhaRuWQSmWR4fs7xGC41wPOb06Yh7i+pHHdlZQVZJKDcDeOjPJdlvreO5SvI3vq9/3RaOcF7SrQ5LCefGK7Lwm28KQYvs30uRdeQFTd5ozwZP4Ys1M0JLxtm/08EU8hmrs8gm3uJ7d3fHudxjyCSphAn0Fp5GFm1wu1O3s/Fm8T43gaVMTGXnkU2pCygN1Y4Ceay7HMzNZ7fd3/kPcj8FI/7O5+5geyt72DbNy2xH7w4m0UWbOJ8eXScc+2f/+ZfHLgx0dfWJD/3PW+vy9aDXLD90blbyFrWm5E9fITz9Yk429bf5B9F5ThvxkubTcgCgQWeSwuvgTbPJVKDyK5uTyBbn+KCrn9oBFmstQfZpZu8zC7HPiw7fL/tYY6xjOP9qSK8z7b3diNbaeK8Valw8VYr8/7eusJz6evm+51Z4PVo7+E4zir3uycn0q86JuwrKsMwDMMwGg5b4BiGYRiG0XDYAscwDMMwjIZjXwcnHI7KQHysLqsET2K73uEWZJ0tm8gmy3Q+shP8jjPeT19kaonfvSXi/M4vEuB3g1eu8/u9oVF+j3rp2iSy1CC3m7jF75KX1/k14PAwvYTpTfoQLevM5pfp0QTWeH4jI/xOd3aW7k98hd+3RpP8XjukOEwLi/REWo++DVkqwHOeU7JkD7/n3cjQcziIuGpAQpv13ysfOsQxsdz8MrJEjd9Hbwwocu5hRkuTHKpDCcXJyuUQlQt0AYTDRNbb6Sq85wG6MCFFKL62zrE9UNlCtlmmczE23Ins1vUpZP29/O6+0LKOTKIcszPT7P9DI4eQ9XTQp9pu5v62N84hO/8ifcJDI8yCnn7KeDvboFTinHcQiURjMnyk3hv7xOWL2O6hjlFk3SO8TxwdVFzIG5zTfBMdqkMVXtP2CF2dtKIfNkU5xoKKAO8L28iOhTlONoY47m5O8T4xdJjzQrKLPtBFxV0sKG5qE5tPJKvcPxVTuCS8V25d5vxfU+7RHVu8HodbOVe0eo7FSGwD2dQG2/7o4TFkT06wb7yCfYJjGIZhGEbDYQscwzAMwzAaDlvgGIZhGIbRcNgCxzAMwzCMhmNfyVjKXgLz9YWE0krxuliUQtSWUtUzpkhcPSNnkFWbV5G9rYmVGEuKPJne5GtH7j+BrG2ZxbbOxFkMMTDQi6ywk0fWP0DpalWpRDo8wGN4RfYaSlHYckqFyrhS7LBcpBQ2oEilmSrbbzDJDdeaKJE7pWBhzlGWDgUUsXyUMuu68t4OIi4Yk1BHvVAZXlFEumMs0rbxlS8hGzhC4bppizJtc4rCbi63hCwR4jjp76Rk6VI8hrRSdpypUP6O+jZkhTzH3a0SheLxMIt3FdaVwpQFFhe7tEhht2+YDxoElLFdbGcxwdwix05she1XDVEKLmyzr5eU6tDtbW/h/kYp1saUvzUzi1q52INHqVKW2TsKk/bssF8/eJxzX6mbc2lpg+3TXaTYGxwZR1bd4LWfK1FgHYtybi6s8oGXYaUif9VR2J2ZYsG93jgfAjge4v6WF1l0dnuHY7bW3YEsH+H8WqhwfCZSnCvCYc7DW4vs62dO8h64usz9pdop6EucfbiYVa6lMv2nlHvlglLobz/sExzDMAzDMBoOW+AYhmEYhtFw2ALHMAzDMIyGwxY4hmEYhmE0HPtKxiWpyqzUy40jCVawbWrmblqiFFODimRcm2Flx/ZmSptrLz2PbP0Cf9E38YEnkLmbn0EWy/L8WgdpOm1+ir+6PJbgry5HVykY9pYoywVKlCwV11G6lYqNuRnKaG29FNlCLRTPtq5eRdb1tvchy7ZS7IpU+N6qjtUoYzVet2ScbTWfpswXdG+MtXYs5OV4X337Xl+7ie2GhZVN25TqpJ2JU8i2iuyHrdemkB2KURIs1yiJVya7kC1HeA16K+xfq1nlYQHlUhW32NfF8RiHg/ch29pmu+z0sMp2zCm/wh1S5pQEpcjAEmXWsFAoDgYoWTZFKV8/+LaH+NohtlU4NYrseITX9/mvcH6bm+AvVB9EvPdSrtZLtg+c4bhfXVOE3XW27eYSK7Y3KRWFm5QHT3wz5fmhCvvX2ial/bY+9hvlUslylv2wpVnZX4znt9RFUXi4hUJ9p2f7bc9R7J25ybbaHOdDNUvCB0qa5ikAjw2zMv76Kh8g6FF+eb29xvFeWVUeWulm1e6K8isHnS28Rxdbme3HG+OuYhiGYRiG8TVgCxzDMAzDMBoOW+AYhmEYhtFw2ALHMAzDMIyGY1/JOBKOSCJRL7Fm51nZtLmZstJIiCKRjFLC64sPc39TlDaX5liNMnnfY8g2rrNiabmQ4/4cpbCWdUSSD1N+kmcuIeoqjyLrj/Dn7aVM6aojwSrI+S1KwW0FvvbmMy8j636QbVpbvIxsK30/MtdB0a5fEVdnhJU7h1MUntPzrHw92MQqoh3ujVG11UWcRIfqRdRjbYPYrt/TTrzWzQqjLswh2L1OIVACbLPwAEVc79lfp0tnkdUiPJf1MGXCQyusFhuKUYpM9HC8t7ZRIL1e4vkFNYl9m+MzqYjHkXVWNo1EOEdNhtm/mptYaTkaoLjd3s/tOs5QFk2E2A821yhuLwQpMh8+xDF75tgRZL/xHz6G7N4TFvF3PpygVIlupoi+tMy26OlPIJvfZHv7db7WC9s7WL27uWVzg/3aOZ5LgO6wRJo4jrMLPJdQkBJvfwcf7FiamUI2kuK5XJjicafTHGMPtvG1ayFKy9evXEM2rlR97lEqtQdyC8yUc14pskpzYZ7jeCfPOWA4xTlA5JyS7R3/Vf/FMAzDMAzjDYotcAzDMAzDaDhsgWMYhmEYRsNhCxzDMAzDMBqO/SsZl0qSTtdLomMDFN9aEhR/MgusHNoPEU2kmptC1qFU+43fTxE3Xe1GNnCcprA/y+qHM72sFLmjiIghRzFahtgGeaWq7MTAO5GF5yhiFQb6kbW0Uaruuc6fmW9/F2XWwBrPOdjJ9zvUwtdmwxSKF+ZZofJoipWWgxlWNw6FaOQFUswyL1IYPIjUChXZvFrfx7JV9tdihf11PEFZr7RMqe/6DOXcSIBZ8QpF9EAzJcYBpUJrtUAZf2OF16XnBIXAlk721/UtVjZdrfLvp4EB9sNajYKhUyTt9QX2w/XzFBFPflc7sh7Pas6iuKfRYbZzZ4ljcbybx5ioctwNt7EfFLYpkSe3WIF3s5Xv7SASCDiJNN0x/+V4/RZqlIyTCc7hfofXYDjKhxWaPUX5xUE+6OAUgTWRotQtiqDvqlPIyhUeo1ZhZ1pfZ7/ZKXEOX91kf+g5TonXK/PwB9/Je++F8zzu4R72r8gwBeDlHEXrsHIPnK3MIAtUON7jVbapVhk81sZrqc0VyznF8N4H+wTHMAzDMIyGwxY4hmEYhmE0HLbAMQzDMAyj4bAFjmEYhmEYDce+knE4EpZEsl5ESis/a36kxiyRoMA0naYoNhbia1du3kBWmGd148rDFNRqq5R95z0rkY60UrwsCAWwzWfOIxscHeVxWxRBzVPOSis/ed9WUKoCd/G9Tc1PI4tXKaNlFm4hG1Kqok5e5XsLlimV1o6eRLYRUCTCIAXlkT4KeXOzlOXiA+wvB5FqrSab+fp+Egnwff/V2b9C9tYRtkVbCwX4E+OHkc1PUZRsbqboKi0cJ2vTrOy7eegQskNHWY04GqecGA/y76LEEWYL0xx3X/rMU8hOtVEAPnX6PmR/ef45ZCtLHE8Pb70XWcdxzgvFKY7Z9R2KvfHDbOeNJYqS3YrInE+zmu0ZRZ68sMLr++THryM7iARiIWk5Xt9PVr78DLYrrVJgXY3w4YLqJq9VvIV9SWq89n1l9temEYqplYVlZBHHcVxr4wMEkQivfSbHqvXt7XzQprRNAXh9lQ8fjCrVl2sjFIpbHY97aIsPAcyX2Q8La5yP1pZ5j+7pVaovKxWeC/kJZLdu8l506DDnNz56IxJW2jQU+toeRrFPcAzDMAzDaDhsgWMYhmEYRsNhCxzDMAzDMBoOW+AYhmEYhtFw7CsZu7KT8GK9oJXqouQzpwhRTdpPxY9Sktq8zmqs48r+1sMUsTZffBlZYOw4slQr5cnsVyjBpR4aR7bcznO5tTKJrGme0u1IH9sqmaG0FlCKM2bP8Sfg+xTRLrfyN8jinZTH5mcogK0VKJ+OTFCg2xLK0stjbKtgBytVTymCazDKKsgrQQqDB5FizcutQr2I3TNASbw7zmzjIQqQ/XPs1+n0FLLR5CCyi2dXkYWK7COFTsqT8cOssDt6guJxd5xie1YxAlvXeNyn/vSzyNrWlarPxzgAqkLJ/tgDD/C1SxQRoylKkaEZjqejjyl9uEgBfn6BVVZblPcbGmA158Qoj1Ft4cMMT3+U42ThFt/bQaTmRYp3PHzyTJnX+e2neE07lgvIFmocE84pc5pnv25qZl/aWuH81Run6L2i9Gu3yGtaFs71tT6KvRueD7Js1Sge1xRReH6RQvbwMM9lvsJ5pucwBerNOc7ry618oCQZoTx89TrvHYOO7Xx9m/16VHkgp6pUN+7r470jozxAkOrnvLUf9gmOYRiGYRgNhy1wDMMwDMNoOGyBYxiGYRhGw2ELHMMwDMMwGo59JeNKNCArI/VyUrx9CNsl5qaQrQ1TKEvVKGcFhiken1VkpWNvf4jHuM5Kn7kgpeDkKM95vZlru44hRX5Vfio+laTwtl7l/q4GKBl3DnO7oLCK6XIP26/SRslsc00RTUd4jM1WVjI+tcIKxWnl5+gLTZTREhFFLC/zuDOKkDfASG557u8gEo0F5NCResE0t8Q28ylKwe84/BiyrtOsfrt6ixL75acp+3bfT/l18/wUz8WzcuhgByXLQJiW5UKNfTjQw/7/9G9fQNYeZQXgo6coOx5/hJWHX5ph1eKhLs4VHQ+zkvdmnnLi/e88jSy8zvdbKFFwbY2y/ZxQZB5UqnunFyiQdioPH+QWKYYODHBMTND3vOeU8nmZee6luuzBfsqqixkK3KGeDmTxJc4jtRpl2mAwi6yvxuNGhf3VCyXo7v+fvTePkiy76zt/N/bc94wt96x96areuwXadcQqFtkIEGbAngUzZjyMPQMGMxj72MD4DHOwBxtmOGODsUASOwZh0Iq6pW5Vr9W1V2ZVbrFm5J4RGXvc+SOzIaM+t0PdUkudHf37nMOh9a0X8d677977bkZ83i+6ed/xd1JkXlnPIetb4hyZqHAflVE+aFAps990D3NemJ/jHDA75jjfDgrK5Sql+Isetv1Cif1w1LGPpRW2ff8gj9mVpVLs1z7PqxTq07xXtkI/wVEURVEUpe3QBY6iKIqiKG2HLnAURVEURWk7dIGjKIqiKErb0VIyDnk8cqyr+afrVzYpIVWFItasYWXHmkMUy6xTiJq5+BCyHcfPrqcd0nJsjHLnc3eWkU3GKB4vbVEyy+xUkIUGeL5rDYpTIT+bN1ejoNZwnMdgHyXjKwn+lL3fy9cu3qYUNnuGx5zx8vjGRimfzntYlTqT5mtDdQpgMYeQnV7n+zXSPL6jiMcblI7BZrF1/foz2C4wOoVsd5NVTGMN/o3xDf0nkf3+OKW+zIsci5HjFOUvDlGCfiTiEC/DlG5rNVZj/fy1eWS+ZfbrAUeF51Pf9T5kPZVtZKeLFKh7TzjE+zAzzxplVr9hf31xnu33gROslFqsUSD1OcaE+KYQDXv4kMILL/G1m+sUo0txiqFHE4/Yeyrbej1s755OXuetdc6bgxOOOSPJOXzCUd379twcskCQYvvxYYre270ci8M1Hl93B0X00l2+djDsqFy+zYdC5roo4xdqrPa7vs2HTLyWc25nJ/vNxATbYK3O6xHq5T3w7nXHAyXjvH9OdHQgS2cdVZ8N3894HW0/yPaTDbZ9K/QTHEVRFEVR2g5d4CiKoiiK0nboAkdRFEVRlLZDFziKoiiKorQdLSXjaq0qubVm8fDEOVZjze9d5hv7uHZKZCnXBQzlUo+l2NuIUjgyayvcr4dy1gxdNFleorTs91PGHAixifoCFNSqjoql9RrPbSjM46s5BNusQ3gem6Z8t+moPutP8pgDNR7zUMQhgqeSyKYd4nE6zbaPxig3Vxo8N1fNYhN1lDc+gtRqNdnMNQuhfkdp5lqF17lRd4jeHl7TsS5eq28cpHj8XO1LyG4+y+v3/u85i2zRy7E4sc1jqViOiYj0Ixs6QQFy+Pg3IvN3URKc36J83TnJffRMO6qiLlOyPDvDysPXX/w8sr1NtvOmowJ1I8vK0uEI+3oiu8t91Clah4dZQTZcZ3/pSPGhAh7JG4+p1cR/z8Min738Ara7cIZVp886pPiVJZZrng2yH0qGffj0Ke5DMpTO19fLyOqREWTXk+xfvvAxZKud3MeMo+J3eZBz6ZluiujPPvUFZD117iM4wAr1Lql6JcGHFCLH2KarFbZLbpsPFYzFWAV/7g63qzvmf/8Eq4DHAnyYYW2VDy6MOx4iaoV+gqMoiqIoStuhCxxFURRFUdoOXeAoiqIoitJ26AJHURRFUZS2o6VkHAwEZHKyWeoJOKTbZGoK2ZCjcmJnxwSy3R2KSQsOISo0zX1cfOztyMRSajLC/Y6OUQz1OtZ7q8axBsxRfjr/4OPczkEqQQk08giPOeqliptKcbvBBs8tEHVI0A55bNFDUTg2QXnS16AAWfZU+X5Zx/uF2V+8hn1jzFHJ8ijiNx4Je5srhZb3StiuXqcoPzzIbGiEbZvaoLA+9s4pZOnFJWSeNfaRDcNq4UMj3C75HKusTo6eQnamg0Lx0Lvfi2wwxirDN4usnvrgfeeRleusHrz+HKXbUIHnMee5gmzNcEwcu5/idmg4z/0aCs8Jh+DabVgFeWqM4uqVSzwPj+E1z/M5gyPJzm5ZPvmZ5nncDO5gu1MnKACvrfIBlUaSsnZ5nA9EFDo5j9g1VqcOeyjTbuywvWuOvjl+/kFkm+Yisv4Jzq/rT1IJ7xugTOtxCOZTXawKnNvl+Sa32F/HIpTYB4cpPL+wynvvc3Ocy0ojfO2wo3J/IMZ7qm3wtZkk+39tnOLx6AiveSPJ+2cr3hx3FUVRFEVRlNeALnAURVEURWk7dIGjKIqiKErboQscRVEURVHajtaVjKsVyWWbhSB/nT+THvJS4rpx+RqyjR7uI5ejcDTQw6rF5xpxZJUQRaxymvLf8CiriXocktRqmuu9gIfiVHyMx5LJUIy2EVatjI5T4m1YR21fRwVIX4TbxT08Zo/hMacs5Sxfg8e35nitJ7uGLOTvRDbhqGScTrES6PQkq1HOv3gT2VGkbhqSDzVLkIU8K/FmvMzObLK9UwlKlhceuR9ZdpNi+7nHLyA7X+J18XophBcLFCrDk1PIuursD391fQ5ZtZOv9XopCp+Ms8rw/A3KmOUqDdtTIbbf+jYF6rUUZcxjuxw75TIrww7sskrtto/yfFeB+1jv4Vw2kef5hquOsR3gOI6FWbk5t0yJ9o2mWKvJ1Xuqe3/TxAPYbmCPQuxqlUJs/wVWN5aTrNjb2KEUX16g7FsMs73XVlkBfiZEeb67yD4XudCLLPX0IrJVL4XdTzz/IrL1dY7tqTjvgYOj7HOlEh9cSO1x7tleZ1uFOnlujTSPr+EQozO73Ed/iA8VdHiWkVXrjmWH42GehOOBHOO4H7dCP8FRFEVRFKXt0AWOoiiKoihthy5wFEVRFEVpO3SBoyiKoihK29FSMjZGxNwjnfrGWBE0vEBhq77LapQ7RVZO3LrOaqy3a5TCbl5+CtmxY/zJ9sePnUa2Sc9VRFhhMRplRclshueWSVN+8nrZLlEvhah0jhU0TYzHEnZIxqEEJcZ14T7GxinxBoWVIm2KQrZ1CM/RqEMeTrNRV5Z5fBHHa5eWKW0ODLHi61Ek4DES6ww2ZctjrGDbX6ZgmKjyHLtKlP82X6B0OPY45X6vQ57susaqu+Uar7PHyyq+m1lKwWuWkmAsxtcOD/NYqnT7JfcEheIex9iJdfG1XQMUUktd3Em8j4JmgUNb/OsUrZeeouxuz1Lu3A5Q9p3soBybrlHQL4c5xoInvwHZ4KN8mEH+3UeZvdF4rDQ6mkX24DRl96yf7V3j0JHj06yeXa1QlN9ZpSTun6E4e2uVc27Z8tbX56havF6ixJ54lmMitMH5sL+D+333MUrn/3WI5zbUyzncOh4yiVUWkZULbPt+Hx8ESjrar1JzyPhLnK/vhvjZyJAj6/E5HtIZZ/X9smOyKJYojLvuba3QT3AURVEURWk7dIGjKIqiKErboQscRVEURVHaDl3gKIqiKIrSdrSUjMXvF7lHEh3zUQhMhSjhnXrXu5F1ZSnNxSOsAHzteQpbLySfRvbExygo3zh/FdmJ0/x5++MnKQmaDAXN6UAAmXfMJTo5hGKheDkRp1AsQrGr6vhJ+UaNMhoVXpGqQ3Atp3h84SgrT7rqRC4tsZ1d4rFJ8TwWHeJxfCqIrJClCHsUqVmPbBabDdhAFwXumV7+7VD1cbiNe5ltpl5ANlqmPJlY5diZ7eI1XX6KVUfLdcqvEyFKh73dHNurE8zG86zuWqpQHCwPsG/aOg3guTz7w9gOhc/dLR5Ldzf74WQ3qwJ7T7GtPElWhi3cYh8+0xhFtlimHB4Jc664c4kPW3RMsR9ULI/lKDLQ2yMfevfjTVm+k6Lrn764g+zMCQqnK0t8iGNomOL4QD+zjSyvwXSPo4T+g7wXlcucXyMj7JurS4vIlrYpp586x0rj1TKv83tjJ5CdODWDbGfnFrJAgA/VXH2RbVB3zMNrG7weUcf9qZp2iMIxtkt5kw/kdB+neJ8p8bUmyWs+OsKHMoquJxdaoJ/gKIqiKIrSdugCR1EURVGUtkMXOIqiKIqitB26wFEURVEUpe1oKRnbSlUa98g/WS+ricY9XCd5HD9r7vWymuLpc48gG+hdRPb2BGWlJ6OfQ/bFLz6LrLDA9+vaeReyygjFwalxVpD1OeTOukNGayQpGa80KElZh2QcjVAqjTlkzEyGQnbMUUHZN85rlHN4vR4PRbFxRxskHFKYeHgekSgly2SKgndP1qU3Hz28Hq/09jaPgdr169hukS6+hId47RN1luwdnaVgeOfWbWTTp44j8+1yH2/7/nci+9Kf/RmyS8k5ZOer3Ec4ynF8ez2P7FQ/54qxMOX0uwXHfjtYLXnhLmX3ySlKkS9e5fjsHKPMOhnmPvb62H6j6yy3+1SeVWp9MfbhpQKrwJbzd5At3OaYiAZZ3fgo4mk0JFRuFtlDNQrSd5ccD06cYP+PxyndJh3zTUdHB7L1DVYGH3TMrzvHOdcfdwj1ngYfMilNsEp/LEhhvbhNiff4GZ5bycPJorrCvt5nunl8cc7r09N86OFmkdcjVBxGlk5eRuYdp+y7vMCHBaZGeI9+KE6Z++YO+3+1ymNZdFTan/ZyHLdCP8FRFEVRFKXt0AWOoiiKoihthy5wFEVRHRE8lAAAIABJREFUFEVpO3SBoyiKoihK29FSMvYH/BKLN0ui/TEKp9kUK3NOBCl2hfyUqQIOGXnKT7Fr/QRFp7fVWMUxnabwubVBWenzn/5jZCdPnUJWKlCU7B+8D9mFoQeQ+XysDDsyTAFsuUaxcTlBIc8atqnx8HpQRRaxKQrPxlW32FGh2FGgWMQhxsVjfG0i6TCZfaxk7Im/NnnsjaJSKcvS3btNWdcs5b9hoTS3F6Lo13GSEm+9i/JktM4+knT0kXmhAPyubVZy/c7z7K+/mqaguTfIcbe1xTExcJr9wecY28/NvYhsaorXvh5h+53wUwKdmKDEPu5j5eZ0ivL8E5/hXPHO992PrFCmQNq7wWqxG5t7yKrCdlnfYZXfvhLF6JlvnEUmH2H0RuMxVrrvmZt68g4hdoISat8eZdVSqYys2M9rsDZHOT0YYtvedcyvD3dy3G1ubiPrDrLPGS/HsdfDBwOG+vn5wWqK1ZLzVbZBtcK+1LCciCeKjrFTYt/sOcVK47urX0S2t8dq/p4c36/k6K+zx96GbHOTc8oj048i+4Mrf4Gso4/HspLnfluhn+AoiqIoitJ26AJHURRFUZS2Qxc4iqIoiqK0HbrAURRFURSl7WgpGRsx4jHNoqA3Q8lnPM6quwGHvzrlEALFVdnXUZ1xqE7psDA+hSwYZFal1yUjNVYOzV1+GtlLVyhtnjpN6SqTYuXJ2VmKbB1+Nkygh9KmjLMCZKlCqbSeoMTrbVBk9jS4XcNR4TNSpyiczLCKaNQhI6dT1JtjjorMGYcsurrK6qBHEb/fL2PR5nNKOv5O8FV4PlNets9wgK+9kWB16uIwJcuqQ/4O0c+Xpy/fRDb2zQ8j++4PfjeyZ55+Clm9ssz9Bs8ic80BJ47zwYBEku9XXmEF4MlJipzPXXoB2XgHxcvww5SlN6/y4Yi1DMfY1s1byB7oZxXkZ3KLyILdvCALvTy+WJB9wzxzBdlRJODzy0T4nnFuePE300Vkg4VzyFIp3mMmHNXUU/0DyHLbFMd3ermPrgKl2+XrnMM/n2bF6rPv4MMonjX2m/zmGrI7ltKtcTx00T1LwbYwT3n+cupLfO0g54oXUqxQvLDEcZfZYGXp4jL7Zl/nELJ7f/VARCTquI9tBPjwTe85XqNrn/kcsjNTDvG+BfoJjqIoiqIobYcucBRFURRFaTt0gaMoiqIoStuhCxxFURRFUdqOlpJxtVqR1XsE0+o45SdJUAgMGspKDQ+tSCussBsNx5ClHRLv9DQrY/b0UB4zjoqqaYcEN+IQYqNZrgFvX6ewdeMKZcdAgPLY4DmKcRFLgS4SnUI2PU0ZeahC2Xc9S4l3OsBL3YhQHltxVB6OR8PIEklec1cl41SSFWStj8dnc+wHR5F6rS7bW1tN2fQo+2v4uKNac5m2e6mygMz77G1kHQ9SCl5bdmwXvYBsY4LX+fcvs79+2+nzyOIXee2X71B2DO6xuuvVCrOYZRtcuDCK7MbtPLI+RzXb/jy3y24uIqsnWB165hjnj+I8heLlMqvK3njhOWSLexTLu/v5kELfEI9l/SQrHg9fvIhM5BOO7I2lXqvL9nqzPFt3PMAwsMd2zHg47h89QYF7KcF5pBblQysLlykF53YpAL9/gG2bd1QyPt/LKuCnHcJ/4haF9UuXX0LWd/EMsisvXEV2sf4gso25O8iWfZTYt69/Adlzz7JdinusGB3qpAguQc4Bp7oc99kax3ahj+uAWCfnxoUqx05llH3o5hLXAa3QT3AURVEURWk7dIGjKIqiKErboQscRVEURVHaDl3gKIqiKIrSdrSUjAN+v8QizYLRkKM6acpQFItFKA66JOOUozKycQjAE5Osznt3jpKl46XyfJpCZdc25axMms3R09WJ7Ni0o3KzjzJhIsXKjo00hc8nFv8KWan0GWQjo6wKOVTlCZ9/P0W27i6KzCNZXqPePlaM3vDz+nYEKQoHHcLbuNfRxQzX1QVhPziKBL1emelrFuwSJYp5Uc8JZKmneZ0rxygoN3y8ptkvUZScmaIku1UsIRvs5HW+/vyfIzvm20J2/7l3ICsVKTEGtikJnj/JNljaY1Xl3V3KjsNnWdl02zG4bR8rqtazrJSarrCdbZrvV2pwfM5Mce75xIt/hMwz5Bg7axwnyS228wPf+y08virlzqOIMSI+T/OY7uzjwyjLZbbtiQFK4kuuuaCTYq+vk3Nz3xn2pf/y63PIjgdZjfgbp44hqxd2kG3fuIZsM8h5rnicbfDwNPv1zCj7182bvLelMuzXT6wyG3LI18Oj7IfDnRSAt3ZZaVkc98pOP7fzR/l+3jE+eDJk2C7nLrL9rt7godQGHBI0b7N/jX6CoyiKoihK26ELHEVRFEVR2g5d4CiKoiiK0nboAkdRFEVRlLajpWQsVSuSqTVFjYcpCocrU8hWVhyCrWM5FRvjaxMJvtYbpaB2Oc1qovlNVo8828OfWE/kc8h6+lh5tVSgTLWbp7DY201h0VVBObDBapl9vaxuPBZnpchrNyho5vspld78OIXU2RlKxoEAxbPx8Rlk5tYEsqhh1+m4yO12yjy32AxlaeOnHHgU8YQ80nG8WW60C+ybC47KpvOba8iOWfalZYeYV7hCUXLS7xDvF7+ErCvICtgPvZ9C5Sc+/SSy3TIly0fOs+JxucIqpp3bHDvHhylZ3rxBm7BnixWeOzq7ka34KezWOhwPBoxRlFxK8rqVhykK31nisYx/A+eUfI4PGtz0cJ6ZungfMgmyumu2mxVujyL+QEBiY81j+s4K7xP+cc5Bmz5KsucrnDOylteqUWc1dY/H8bCH5TX4td/8KN/vmyjFD/cz21plVeXtGiKRXfalHcc0d7zA+05uh/L1zS5WVTY9jj7skK+liwJwVyqNrOjjTbo2xOPL+1mNu16nFB9Y4/XtPs75LVrngxCDo+wHuQwrMrdCP8FRFEVRFKXt0AWOoiiKoihthy5wFEVRFEVpO3SBoyiKoihK29FSMq7YiizXFpuyrspZbJdZoaw0GWO13wbdLFlJUBTzxigh5fO3kA0UHT/3fuI0smiYgtVzL1HgW04+g2y7h02UWb+OrC5slw//4N9DltukdHWmwp+A/8M/fArZ+97zXmSFvT1k2Qzl5kqZjV9d5n4j8W9ANjs9hcwlghd2KVDn85QNF5Z53fZybJejiDEBCfqmmrK8j334xWuXkZ1/7EFkJZ8XmT/BytHHT7HyaqlI+e/cIPvh8vNPI4u+k4LydIwVsOcdlbdngrzOs5OPIVvIU7Su1Hluo/c/hMwm2DdTSYrMk1FWRb3ToHi8eY1jdmSWYq9/hyLnYo1tEAhxjBV615E9fPpRvnaAEmiywXGcL1GqPop4jEdCHc3SaVeXQ2BNUljf6+SDCVXHvN6XZ5+7WWLF4+VlPmDh81BM9QfZDz/+J1e431OLyMIlHl/NS7k57pDdPz9/B1l2hA+U5HpZGTwZ5H2sbDkm9q7zQRs7xurG3Z3s/zLABxKGCwVkKcf5Lq+w7UfuoyhsBzlvbdd4nx0YPInsetr1mQznmZfRT3AURVEURWk7dIGjKIqiKErboQscRVEURVHaDl3gKIqiKIrSdrSUjH1+K5FIsyRq0ylsF4lQCjYZRzVi+pTiMZRQo4ZlIdOO6oxvP0np1jjWbL4xVtA8kXsB2ZVPPYws7jg3j1BuG64MIeuIstrpA9OUoHe6vwmZmXgX32+L8uQph3xqLdt0Y4MCpEsUHh2hHB4do+w1FhxElnWUqu7s5nbDcQqa+TVWmj2KlIpluXmjWXjvKVOa6+6khGeSlJGDPRwUZ89Qxnz+ShHZ7gDlxMdnKW32jbDa9ZNzlB3f/dg3IstepcC3ZHm+0RK3Oz7LNri9wz43VJ1CdqfA8+0TyqKr1ymne3ooSnZOsHp2NuOQIodZoTVH71jqa5SCi7uUNvseYIXW5V1WRu5xyOZ5++YYEzWPkdVA87WOCse4DLAfrt+lJJu3nOcaJx1VgVcp3a7nHONp6n5kfUMjyBLPcV6/e4tyc4m3E7GOuXlwcgqZ10vZ91KNFc7v3KBknNlgW+1wmMjQCMXe1G2KwsuDlKWjde4jV+Z2M3GOp5093neKjuu7M8rzHe1jVf1SnmPRT3e9JfoJjqIoiqIobYcucBRFURRFaTt0gaMoiqIoStuhCxxFURRFUdqOlpKxLTekeLfZYvJeYHVN3x0KixWHiFgTVgRdq7GiZNZxWA2HwMpXitQclZGN5ZYdDYqIsYlpZLYRQxYOUx72WFYKXlimAHm9wmMxHgpbPQFKV+HxC3y/mzxfn48WnD9AAfLkaZ7H3h1aa7fmeR7JJM93Ks7quOvreWQrG46qlZsUEI8ijUZd9grNEnB/F8/xh85S/j51egbZyjrHTmKHomTxIiv23r1DcfB2lf3hvuMU4P2Wot9nrt1E9o5z7HPDee53N0AhtlCmYB6rsX/dvHMbWTTGPryzQcm469gJZHabknZqheNudor99e4SBeC+KcqsC9cpUM9EecwlH/u13yGgr27yekT6KSgfRay1Uqs3z+3bDum22/DeUQry4ZHdIPt/boESu3VUnZ7yci595wOUX59/jpXsp0+yv66ts994AxSoI7Mc20XHOPFusz/8+TXOpcu7nFMiwjb1GvalQg/HYqXO7W44KnQXrrGdI52cr3NBPrQSiPOBnCuOqv8jN9kG02fYfvFBbnfLxzmgFfoJjqIoiqIobYcucBRFURRFaTt0gaMoiqIoStuhCxxFURRFUdqOlpLxRn5XPvb5zzZlnTdy2G4nx2qPo4Os9FlthJEt1ykj1xOsfnhvRWURkWqN2yWSrE46Nk75b2GR29VHWcXXWApv/hyl27OnzyPb7aYYOlznmjKZehFZLEa5+Qt3KZDGPXy/TIYCXdwhACdWKIplM3y/WIz7tZZC2dz8JcexUGSLzVIEvLnwErKjSEdnSM7ef7Ypi+bZDy++8zFkpSrHxNmzrDwcWKPsm7vKPvJ4jJWMG0lKm196eg7Z6TFK0Gs1Vg5dvUsB+JHzrAybKS4jG1mg7Fvr55hgDxFZXeU80xtjvynepRQZPcbK26teiqF35nluHf0UYW/c4nbFLbZ9rsg5xSY55430sr8EPXwIYD1IMfQoUqvVZP2eSumBDc6R3hir+J47zaq7S1uXkfk3Of+Hp9nX75vivLm2fANZcpf9a8DxIMvMFK/BtuMhieEgHwrZMdwu5aV4fHadVZr3LPtDscIxNlhhX7JVjpOC47UjHvbNFT/HienlAzmNNf7awOQk7zG5LH8x4HqZsvTmNo/v4plzyOp3Ecml1U8wPEA/wVEURVEUpe3QBY6iKIqiKG2HLnAURVEURWk7dIGjKIqiKErb0VIyDgaCMj3RXO32dpJC8bij6mguxd81D8coU/UlWSnS08+frR8eoACcTFJ+fejC25GlLDXG8SKlNROloGZTlK5iFymGJhM832A3JbjcKo85MsoKmukGJT2T4T5WGjyP4eERZHfnF5F5PBSojeH5zudd62DKnZU5ypgRx7E884XnkU15KTwfRRplK8U7zRU2E2G22dK1P0Y2GWKl7IfDlNNPeiiweu97CNkLN68hq0U4Pie6HRVBt1lh9L74o8hCs29DduMyJfb7xnhu8zn2kYaryrCflYJnhlilNm/Z13uOs62KZVZu9u2wMmxPD4XihRItxvAEKwpfr/L9rl1mFeTpx3geazmKq7E4xcuVeb7fkaRaFU+6uRpv1DGndcZ5XeZ3OO53DSXZ1SDb5/gixdTtRx5A1u2Y1ye/lcdSH6A4G0xSRr6bYdXpQR+rB/dtbSJrDFMy9myVke16+EBC1zDl+St3OMb2tlkZOWgoFG+k+YBPF91m6QrxPGp1tl96h/P/6lXOR5UBjrHBHPtBfYtVzyfH2Qat0E9wFEVRFEVpO3SBoyiKoihK26ELHEVRFEVR2g5d4CiKoiiK0na0lIxtpSKNlcWmrKeXP2tecVQsrRpmlTIrSg70U+xaWqZgtbbGaqzROAXWq9c+hWy4SpkqPcJTH0nsIkussDrpiqNSZNghQNbvXEFmUxTKFgYc1V3r3G8yxX1EwpSvb1z9LDJXvdiIOCo3OyRj4/B/k6kUsrFhSuS3rnwS2d7pE8gycQqfR5FitSZX75EMawleq+4uVhhd6k4iG3mc/WHYUU13cpxVYL1nKIlfurGKzI6zv55MUDKev/YMsv4Ax2fvACse767xwYDlWxzvO0t8v9FzlHh3tilt3neaYmOmSMnYeL6ErGJ5zJUhtn1PgfvI7dC83CiwQmun45pLhu1StBxjd29SBN9yPEBwNDFy79/KNs4+V06xLWqGfXjo1Glks70U0Z+5worHz/w+r/2Dj/FYEl6Ks+lP8gGQ7pjjPjHG9xsYZb/eNrw/DXdyzk3NUrp9OMJ93FrmfWw2zH2EHW2/t0dh9848BeWuY/ch83o5p1QrfL9ygWJ5+DyvZTxC6TscY2X8z32a9/IHzr626t76CY6iKIqiKG2HLnAURVEURWk7dIGjKIqiKErboQscRVEURVHajpaScaVSlJWV5mqp84ai2PYaLdTxCOWn3CZfK8KqwHVHheJkgwJfvUGZKplkdcuSQwCrX+KRpA2rZQbGuAY0G6zYeDPLc4vGKFN582zy5A6Fz6Ehyo69vaz6fHeRVWXjhvswhnLWUoNtFbGseGnpfMtgPwXN7ZUlZPUBVnM2I5Sq98YomR1FGo2y7BWbz7N3ehbbvfCJLyKrWF6/nh1WNn34be9FNthDKdJWOXbeHe1DthxaRHbNS2nzeIzC7p0kB0ptmlWLdxsOmXaEMnnOR5F/LccHCN72AGXHtKXY3h3i+W4NsA+PdLEfrq+ySu12jvJkssTKtc87qgwPhfkAxqSjKu8zu5TSu7Y5h3ZYh7R8BPH7fTIy0iyK17I8n2zHALKNLKuzv00oYe8lKVyfCLK9N2vPIStv8+GHE+HjyBK9nNNqBUrQmzlWCu4Ms792jvK1pYU7yMYC7K8L2xR7p4Ypu4+GH+Q+SuzDCxvsw9/6COfcQi+Pud7gQwC9Drk/5XjQZsJRLT/vqF7d5eM4OTY5iSy5QCG7FfoJjqIoiqIobYcucBRFURRFaTt0gaMoiqIoStuhCxxFURRFUdqOlpKx8XvEE2kWm9ZeoCA0OT6OLOWQfa1Dfo3GKV4aSynY56Ws5HX8bH0sTPEy4RDURh0VNCVGuS2VZAVg46EYF4uxDTIZ7sN6KagNO6pWNhrcbypLyTIad5yvS3i2jkrGUUcl4zRfa9e4Dq6Nc7/5WUp6Y+9/JzJfhrLc1iVWfT6KeDwe6egMNYdJytXxOvvSUoZC+Kf/mG3b28d+c9zzdmQTQxT9Yj0cOxslCuanZykA3/RTnj9dpsRrex1Vi0MUOcPH2B8u1LjfQpD9cHeRbSoBitHZIc4pvQ6ZdWeDYzbrqAy+4KHEePMpRxX1Misjn36A1aZf+qs/Qvb+D3wA2cPf/0FkH//IbyI7iphaXUJbzQ9oVGKcH65/iQLw6RluVy6xqvNOgA+AzC2xv56ZPoYss8x589oVPtjRN06p9bFR9s09h8QbaLDKcIfluMt62F+DnRTgu07wPPyG97vCTT6Qs1WjnP5IdwiZv59t37m9jWy5xjmlYnmNHg1Tlm6kWL29q9iD7MpnriHrG+B5vO/tnAc/cuWVxWP9BEdRFEVRlLZDFziKoiiKorQdusBRFEVRFKXt0AWOoiiKoihtR+tKxrYhK7Vmeao+xCq0yw7BqlblT6dLg7srVSgwJesUYuMRyl5FepySdBxLo06RebnBtd2kh/Jr3NFCDYcELVkKi4besdRrlDHTNVY2jVier99QYlzLshH8HkcWobC1lqJ8NxKn8FztouBXOklRbGrqDLd7MY/s7k3KhqFhbncUCYREJo41951ajRVBdxduIJvs5zVN3KCw++QnLyPb7mXV1s5jFAe7ShSUR/v4Wv8wxcaHuyjx7uw5KoOvOMZnJwdKpsH+5fOycvP2LVZproXPIjsZ4jHvrfK1lV2W3g7lObYbeT4wEaw4Hkg4wXZeDVAyfmmBYnR/hXPPRBerOXt9FNBPHz+FTOQvHdkbS7FWlcv3PNgQzPMajDj6sC/Kiu2VEuV0f4aVfc/Mcg6SVbZt9CznpdwW+81GlpWtQ4b9Ie34WCCSoUxbcnx+MBRlVmywb5o65+uFuR1ke3sUnk86BOXNLT7cMhAMcDsf54/TAcrSxTKPr7HBcbIc4D78jqL15wwfZhgeYAXl3XWO2VboJziKoiiKorQdusBRFEVRFKXt0AWOoiiKoihthy5wFEVRFEVpO1pKxn4jEr3HOVrxU4r0+FkRcWKaa6d0kqLTqpdSq99HWSmXpVxkDSUkb4SCbTBEQW3gJMWziPC19X4KxfUkRbZVL2WviwOsjLmyQvEyFnVUgEzy3MInKXfu7lLOtZZt3/DwGu34KMb1xVgF1t/D175t8jyyZz/7BLL1+QVk440RZL17bPtLSI4A1ie20SzE3bjK6p/XkhQW3/NdjyOrFCgeb62zf9186VlktTjl9NkTFDkf7GB7d6xuIAsZPhhQH6F02NtF6dC7TUl2IkzLvrzJc7vdTxHxyqWbyNYenkW2cYPVXSuOSrPGMbZnRvnAxMR7TyIrzbH/nxmg4OrxcdxVxFGRvINzSucu5esbt165QutRIhgMyuxsc78r5CnEnuhk9duAo8LuteuOKtaOavljxjHPOea0VGIR2aiX/WH8LKts+6u8fiHHxwL1UcrSVxa439oW5/UTIbZLcoVt4HX0r5mTHO/WcW7xMbafcTyMEonFkG1s8iGTvhGawokM1waxUT4YU1hiv250cJ7xC8dJbx/XC63QT3AURVEURWk7dIGjKIqiKErboQscRVEURVHaDl3gKIqiKIrSdrSUjI014q01r4GijsqcRlh5spFmBdSYOOREx0+7m1GKWMkkfxY+FnO8NkGJN2lZjXJvj7LjvIfrvWia0pq1bIOCQ0bbzlM+3StRCt4rU5xK1yhiRemUSmKPcmJUeL5JPwXvE5FzyEbOTCPLOkp3fu6ZLyErF9imY3UKdF4P27ThaNOjSKlYlBsvXm/Kbj7H8x4oUNj1blKorIxTdN0oLiJb+jxlbb/j7xN/B8fYzgiF4t4c9/vgBQqLfV2UyYtr15CN9z+AbMRwfNamWI14sngF2Uc3KVWXiqy0fKfAvnTuNMXt+ASF/zEvRcnxLk6Jg4/wus2leT1EuN8LJyjtL6bm+dI6BeoLD1Fc/S35U8d+31iMxyPBjubKzjHHeM4F2F97ipy/6o7K6bcucf5f7aLoffIUq/hWVzn3Pb/Dh1Y6d7jfqQj7SIFTvYQMRfnKMB8e2bpxC9mtDlbFnp2aQhZwbLe8wv4fi7Efrm/wfCOOXwfI5fhwxKRj7CwUec8fHmWV/s0cHwKQXj7gMxRmo3ozFI9TCYeA3gL9BEdRFEVRlLZDFziKoiiKorQdusBRFEVRFKXt0AWOoiiKoihth7EOAfev/9GYnIi8NqtHUV4/Jq21LMP7BqJjQnmD0TGhKM284phoucBRFEVRFEV5M6JfUSmKoiiK0nboAkdRFEVRlLZDFziKoiiKorQdusBRFEVRFKXt0AWOoiiKoihtR9sscIwxP2eM+c8t/v2aMeZdX8X7/44x5rte5bZf7b4WjTHv+0pf/yr3ETTG3DTGHKlHTpWvH636mTHm7cYY/nBO6/f7DWPMv2zx7yMHfa7j4H+HjTGfN8bsGmN+6bUd/as6nl8yxvzo6/2+SntijDlpjHnxoD/+w6/TPn/BGPPjh/73jxpjssaYvDGGP0b21e0rbIy5YYwJvp7ve5Rp+WOb7YS19uxX+lpjzH0ickFEPvy13tfXC2tt2RjzH0Tkn4jIP36jj0c5WlhrnxCRk6/z2/4TEfkNa23x4H//DyKyJiK99mtTr+L/FJFLxpj/z1pb+Rq8v9Je/ISIfNZae/HrsbODPy7/GxE5dvC//SLyf4nIY9bay6/3/qy1WWPMZ2V/3P3fr/f7H0Xa5hOcrzE/IiIfeT0mYWPMUVpU/raI/NBbaUWvfPV8JX34oI/9kIgc/pR1UkSuv9K4+mrHirU2LSI3ReQ7vpr3Ud4yTIrItVf6R2MMf976q+OHReQThxb8YREJvdIxvE73jo/I/v3sLcGbboFjjPlJY0zy4GPEW8aY9x7654Ax5j8d/Ns1Y8xDh1731x/HH3yd9XvGmI8dbPu8MeZCi91+i4j81aH3mjXGfMYYs26MWTPGfMQY0/9l9vWfjTE7IvLDr2X/xphHjDFPGWO2jDFpY8yvGGMCh/7dGmP+vjFm7mCbf2eMMYf+/e8dfCy5aYz5C2PM5Mv/Zq1NiMimiDz25VteaVMeNsZcP+gf/9EYExIRMca8yxiTeHmjgz79k8aYl0SkYIzxGWPuP+i7u8aYj8n+5PxKPCoiWwd9TowxvyH7C56fOPg4/n2vMFaCxphfNsakDv7vlw8vyI0xP3EwLlLGmP/uYDwcO7Tfz4nIt71ObaW0KcaYz4jIu0XkVw7644mDr1x/1RjzCWNMQUTebYzpO7jH5IwxS8aYnzHGeA7ew2v2vxZdM8YsGGN+7KA/vtLC5K/vK8aYEyLy8lfCWwfH8/L8/g+MMXMiMneQvc0Y84wxZvvg/7/t0HlMm7/52vdTB/eDw39UfElEZg7fB9qZN9UCxxhzUkR+TEQettb2iMg3icjioU2+Q0Q+KiL9IvInIvIrLd7uO0Xkd0VkUPY/yfgjs/8R4b377BKRafmbziciYkTkF0QkJiKnRWRcRH7uy+zr9w6O6yOvZf8iUheR/0VEhkXkcRF5r4j8j/ds8+0i8rCI3CciH5L9dhFjzHeKyE+LyAdFZEREnhCR37nntTdk/+s35a3JD8h+f5kVkRMi8jNV+esEAAAgAElEQVQttv1+2V8s9Mv+3PFHIvJbst+Hf1dE/laL156XQ2PIWvvDsj8W/rW1ttta+6mDf7p3rPxT2V+AX5T9fvrIy8dojPlmEflHIvI+2f+Y/12O/Wr/Vr4s1tr3yP78+GMH/fH2wT99WET+lYj0iMiTsv/VTp+IzIjIO2X/K6a/e7Dtfy/7i5aLIvKAiHw5Z/Ovx8TB/l5WG/oPjudlvkv2/0A4Y4wZFJE/E5F/KyJDsv+V1p+Zv/F1fltELh3828+JyA/ec541EZmXt8iYeFMtcGT/Zh+U/Qvtt9YuWmvvHPr3J621n7DW1mV/4m11EZ+z1v6etbYq+50kJO5PMl7+ZGb35cBaO2+t/aS1tmytzR28/p0t9vWUtfaPrLWNQx9Hvqr9W2ufs9Y+ba2tWWsXReT/cezrF621W9baZRH5rOwPMBGRvy8iv2CtvXHQsX9eRC7es3rfPXSOyluPX7HWrlhrN2R/Iv/+Ftv+24Nti7LfV/0i8svW2qq19vdE5JkWr+2XQ2OoBfeOlR8QkX9hrV09GGv/XP5m0v6QiPxHa+01a+2euP/I0P6tfDX8sbX2C9bahohUReT7ROSnrLW7B/PxL0lzf/w31tqEtXZTRH7xy7z3qx0Tv2Ct3TgYD98mInPW2t86uCf8jux/DfsBY8yE7P+h+7PW2oq19knZ/0P/Xt4yY+JNtcCx1s6LyI/L/kS2aoz5qDEmdmiTzKH/3hORUIuPB1cOvW9DRBKy/4nMvWwd/P+elwOzb6N/1Ox/VbYj+17BcItDX2mVtdr/wUelf2qMyRzs6+cd+7r3vLsP/ntSRP7NwVdXWyKyIfufPsUPbd9z6ByVtx6H++aSuMeAa9uYiCTv8Wda/eDiphwaQ69yHy/v5/D7Hj7G2D3bu8aZ9m/lq+FwnxqW/UX9vf3x5fn01fTHw3wlY+Le8XD4GGIisnGw2G91DG+ZMfGmWuCIiFhrf9ta+42yf/O2IvJ/fIVvNf7yfxx8hzomIinH/goickf2P75/mZ8/2Pd5a22viPwd2V84vOJhf6X7F5Fflf0V+vGDff30l9nXYVZE5Eestf2H/q/DWvvFQ9ucFpHX3dhX3jSMH/rvCXH3wZc53I/TIhI/7HsdvP6VeEmax9Cr2YccHM/hTxwPH2Na9sfNyxw+l5fR/q18NRzuj2uy/ynOvf0xefDfr6Y/HuYrGRP3jofDx5AWkUFjTOcrHcPBH/zH5C0yJt5UCxyzX6fgPQeSYUlEiiLS+Arf7kFjzAcPLviPi0hZRJ5+hW0/Ic1fC/WISF5Eto0xcRH5376G++8RkR0RyRtjTonIa6nr8Wsi8lPGmLMiIgeC3Pe8/I8Hxz74CvtV3hr8A2PM2MF3+/9URD72Kl/3lIjUROQfGmP8xpgPyr4f80pcEpH+gz73WvgdEfkZs19DZ1hEflb+5kmsj4vI3zXGnD6Y1P93x+vfKSJ//hr3qSjgQH34uIj8K2NMz8FX/f9Imvvj/2yMiZv9h05+8su85b33lVfDJ0TkhDHmwwei//eKyBkR+VNr7ZKIPCsiP2eMCRhjHheRD9zz+kdEZPFg27bnTbXAkX3/5hdlfyWdEZFREfmpr/C9/lhEvlf2Pyb8QRH54IEP4+L/FZEfOPTX6j+XfYlsW/aFrz/4Gu7/f5V90W1XRH5dXv0NSKy1fyj7n3B99ODrrauyL8G9zIdF5DetteWv4PiV9uC3ReQvReSu7H9S+YqF+g5zUFfmg7L/qOuG7PflVxwHB9v/hux/2vla+JeyP2m/JCJXROT5l4/RWvvnsi9bflb2xcmXF+plERFjTFT2J/8/eo37VJRX4n8SkYLsj5cnZX/8/IeDf/t12R9LL4nIC7K/GKnJvjvq4j+JyLeag8KXrwZr7brsP1Tyj0VkXfZr93y7tXbtYJMfkP2HUdZlf5x8TA7Gw6F//7VXu783O+ZrU1/raGOM+TkROWatfdWTrTHmt0Xk49bar3qy/Er2/3pz8CnYZRF5h7V29Y06DuWtg9kvbPaEiNx/SLZ/Pd//tOwv4oPW2prZr458x1r771/vfSnKl8MY8y0i8mvW2ld8JNsY8/Mismqt/eWv0TF8TERuWmv/mTFmVPYfS7/fWlv6WuzvqHGUis4daay1r6qK8ZuFg09tTr3Rx6G8dTh4Cup17XPGmO+W/b+UO2X/08r/cvDEoFhrtUK38nXj4JOYd8v+pzhhEflnIvKHrV5jrf3p1/kYHpb9T1QXROT9sl924RcP9rUq+07aW4Y321dUiqIoh/kREVmV/a/X6vLaHDVFeT0xsq8vbMr+V1Q3ZN8Z+3oSkf3ilnnZ//r2R621L3ydj+HI8Jb8ikpRFEVRlPZGP8FRFEVRFKXt0AWOoiiKoihthy5wFEVRFEVpO3SBoyiKoihK26ELHEVRFEVR2g5d4CiKoiiK0nboAkdRFEVRlLZDFziKoiiKorQdusBRFEVRFKXt0AWOoiiKoihthy5wFEVRFEVpO1r+mnivz2NHAvds4uVvV/lrNWQew/cL3fteItJo8LU+8TqOps7IOLZz/LRWw/F7W41G49W8VIzjWFzb1Ro8voZ9detH6w0y6+zk+5V2kVVrbOiqZebzsp3zjtdKg8fs97AN9rxlZCHL7WrlCjKv37Fdle23ZWXNWjvCg3zjCHWEbHdPd1NWqVZf1Wtdv/vmuAIScGQmEEJWdew3EOA+qsax3xozn2tM+Hg05Qr7kt/HM7GOkVKpOPZhmfn9fsdrHe3s2M7nGKAVR9vbiiNzbef6vT7Lfv1qcb2fz8e5seLYhy3bIzcmvF6v9fuar4N19OxqhXNGb1cHsuGBPu7E8tq7+pfP1V/LfK0x7HOhAPuSC0e3kVf3SpGq415pHR3WU2fmmEplt8h5s6Pj1R1Nh+MmHQryvlP1ch8ewzb1We636pjgjCN0tYELv+OaX19IveKYaLnAGQn45F+fGm4Ou9kxIpuryEKOSfXE9ACy4m4O2ainlwdjNpn5u5mx/8hukcdcLHOx4OhT4vVwsFUdjbxZ4PEVKl3IXIOy3juLrPbgg8h2bn8O2WqOnSVb4SAf7GU7f3GTHbK2y7aPdrOdX+y7g+xUmW21Mb+ErC/O7VaTG8j+sCJ88RtMd0+3fPv3fEdTlkqnsJ3rJlZzTG4+w+s37vhgNTB5DFkmnUUWjXHiWfU7FkKbnC37i7wBVQfHkC0vcbyHR9nnanXud2GlgKxe57GEw2FkqVQGWWM0gmzEsXhbqXGSri7z+FyLRlfWqK4gs44FosdxfcsVnu/I8DCylRr3UZorHbkx4ff5ZSza3E9qHs4t2eW7yN5x8Qyy//ZvfwsyW2RfrwrbcXh4CtnCYgKZx7OH7OQU+7o02JeSjvtExLUGZiSZVZ5HfZRbduxwLM4tc1548kXOm6cuxJB5HX+4nu3mH5qnZy4iy/TzXhkwHIsjlmMx41hEeTM8Fjvq+MOnzjEbdvwxdOEHf+YVx4R+RaUoiqIoStuhCxxFURRFUdoOXeAoiqIoitJ2tHRwQqYqp/zppqyXX4HJ2PlRZPkivx/1pOiBWPq1Ig36LCWHYOXxbCMrOxwcx9eZUnD4ijXHuXl8DOv8ilMcKpEUqvzushbrQbaXWEd294VPI+tL5ZHZqkMgrfJ72drmELLjMX7v/4TlPm52bPFYioPIMhl+1318jPu4k1lDFuDXtyLLjuwIYO75XjkWc3QIB0sO9yLi+KY+5XBrJi3bNhbnd+h1h5g6WKFXtTHC77fXMvy+vD+7iCzqON9ajcfsUqjHYnFkdYcfs7LCiz8e536rVZ7v4grbKhpmB1tyOELRMLNalZNKo8o5L5VM8v0cbVWpUBmwjgcDOtfpNZWkhOyNplqtSjrT7GSMROmzuNpi/cYOsvm7i8i+9V33IytU5pDdLlLGHxo7jmwpOY/s+jLdst7ZaWTeVW6XcfhXtRF6r41pjkVPiI5jxzbfr8uh0VYc/eGLjvOYanD+f/8PjiPbrXD+P97DY17PFHl8Ht7bpuOc3zJ7aWThHsd4cjiLayn6jq3QT3AURVEURWk7dIGjKIqiKErboQscRVEURVHaDl3gKIqiKIrSdrSUjDuCImcnmrN1h8Rrq7R47yxRAK47Xjsz1I9szVHVtuhxVRh1VP90LNnyjrOcT1CcrboE6nG+YSLPY8k73L+S43wHy3y/lSKlyPXbfG3GIWQfD1MeCzQoAOe8FFKfLVBG3uxxFGfL8bUdhqLYhwYpkG6mKF6GHEJx1HHMIjy+N5pKtSrJe2RS6ygG5iro2KjSElyuU6admZxClkxSRM/3sH0mQyyQ5wuyc5bKLLrW7WVhyp5+Fmzb3aIkW+3hOE57eMyjjqJ+9YajQziqbjYcE0hieQFZzPHa+vIiskiVfa5R5ySQTXF8mjCl4A5HJdy8Q9C3m5Q2Y3G2nx3k8W088TSyNxq/3y/RaLQpS6R5DbxC+XXbURHXrr0DWdIhwHcW2Nf3CpRQ12OnkUVPPIRs5fbzyAb3eB79cVZfFstjMY6idDbIe+XqBt+vNELxeGiP+zj5dh7fpT/9M2Shk5ybB2q8d3SHHNWhuygPezsoI69ZnkfD76h67qc8H1jjXDE+QvG4MUYxuhX6CY6iKIqiKG2HLnAURVEURWk7dIGjKIqiKErboQscRVEURVHajpaScaPmkb2tZvEwk2R13mSQQnHR8VOq0fEJZNkdVrKsV7nuWljkPhxFfKUWpZjXb/l+aw4p2PEjxNJVpGC7bihBD9LXklW6iRJ0nNvAKGWqvSyFvF3uVm5WeXweQ4lrdZ1CasJPa9lb5S++19MUYQccVWXzGYrH0xFej5JDAk2YN8daO+D3o3Jxw1HF1FrKjg3HL/rWFh0yppfblQYpoaYuXUPWcYzSZvdJvl+0xA6bG+J2q7uUCcM8FEnvUiYc76UouVhhfy1WOKdEGnw/u8Jf145ZDoq643r441FkgQx/EXm1wOMLn+MvXvu9HGPeqGssUo5tlNk3qnv8lfXTjurLN5G88VSrVUmnm8e+iXJ+MIaia2mF0m3CIae/w/FL9zsF/iL4QB/nL+nkZL/d04mse4T3p90VXqtQgfso9rC/Tozx2uc2OO56OzkH+CNsq8YtnsfpDt7C7f3cx2iMFZ57hjg3+yz3YTt57605ROHoKDNjOSZyHh7LuuMXA8I+SsuprOOm2oI3x11FURRFURTlNaALHEVRFEVR2g5d4CiKoiiK0nboAkdRFEVRlLajpWS8V23Ic5lmATDvkOuOT8zyjcuU/166Q0Fob4uiWGTEIUDy19mlRIdLRhyljHOWGwYdUvA6HVnZKfO1u1lmgfgwsj27hqzqEE2XMxSKXfUa+2vcR3mYMtq2ow0685S4IiGHVLrIKrUXoqyg2dNJQdm/S1kuvctKrhXHsUTDrPJ7FKlUKrKSaO7H0YijEq+jkrFxCKeOLicdNVb7rTky/wRFznCEAqSZu4NsrrzJHUfZv3ZzWWSd3ZQEPT6e70Yn5flOhwA/WGI/zNBNlLExVgruC00hyybYqmMOGXk1xuv2YJQycqrBatxecZQpT/H6Wst9jMxOI6tWKYcvLfKaH0X84pe4uUe8T1POTTv6fzjOueXZFynP3zfdh2y4TDm96GPfnIo9jmyzl4Jt/MJxZMtf/DyyTI5jp6uL5v3llyhLnzt3AtnWJm/DIxVe+9UiRfR0lu08Gr+A7MHpR5GZ2Ulkfofs67Hs/xO77K/S56hm7ngwYHSK64VkjdstVXmPqccogrdCP8FRFEVRFKXt0AWOoiiKoihthy5wFEVRFEVpO3SBoyiKoihK29FSMpZAUCTerLtmLCXB9NUFZKUdWsGOYpTidVQe9pW57vIPU4BsOCoKb9SYpTKUwhqOqsUxx0/e7zk2LFrKY3XjkGQdErTPwyb30p0U7ygt6J1VVoYdHb6IbM1RtTJ39y6ygavP8/hm2AZrq5SlH52kLNczyMqT1kNdeitHWS5ZdXSOI4gVK/Vas2C3vMxKzyZGWTWepaxnLfvX3h1KwUOj7F+VPUqWjsLDktplFWuPl+0dtxQbPQ2O43lH1d2oQ1D2VnPItjZZKTUcoRQ54e1C5jEcKNlhisyRYbbVYI1S8LDHIfwvU3bsGKaMGRceS32M17e8yAcryhWK4JUyr0e5tIjsSOIXMbHmyS6XZtuOxTghJhwPYng3ppA1HJOkFQrc3gzF1CUP++sD45yr1rfZR3aOn0U2f/NTyKpPP4dsLMJ+OHfnErJeR8XvpefYVtUdDu748V5kvh1Wae7sp6T94hbviz2Oyshdm45Ky9Nsv5Eqx2z8LO9jmTRF5jHHmJU670Ury3wIphX6CY6iKIqiKG2HLnAURVEURWk7dIGjKIqiKErboQscRVEURVHajpaScbFalxvpZrFpuZLBdlGHTNugNyWb9KGkkaAAueUQgMuOzOPYL+v6ikQcVVE9jqqaWesQ4wx37HMsC4M+7qTTsV+/YehznMhamiJz/D5WcXyyxutx5xarW+5co8x38hwFsB4WHpaRfl64nQxlzP5+Cp9SmUeUSlEE94QdpaWPIH7rl3C9WYhrRCjNpRwy3IpDao1FKCPPXbmBLDBHSTzUw+NLBihZjkywwqjPUKhsOLKsI4sM8sEAr0OWzm9wbE9GxpB5PJQnd3s5NY17KU/6/KwCnmywL2VXOU78UR6LhCmu1uYpCs81eN2GaxSoa0PMEkscZPUKJWNjHeNJ+EDHG03NiOTu6doRhxSfafBaeRxV5vMDlN2vJTlvftdFPkwRdFTY7fNyvvEajkWP4x5zops3smQnK/Fu7j2FrJJkn8sFppA9leBDBSe7ObgLq+xLg5btPPDAKWSOHweQLccDL5LkQ0TemTPITNJx/3Tcx3wBisKxIK9Ro9vxgEOGFclPOObLVugnOIqiKIqitB26wFEURVEUpe3QBY6iKIqiKG2HLnAURVEURWk7WkrGtZqV7GZzJdOCUDBcSFEmdDhcMjVO4Wg5mkXWoCMrDhdNYnEKVqspVk8djzkqXjoOcIquqIQ8rKDZLRTeQj7KnSbE7XIFttXMaYpspk6580qBlWtvOCrXPvPSTWTjjkrBoVHKY92rlIe7KnvIKjEKkHfnF5H56ffJWJTSa9Ih+B1FKn4ridFmSc6kHOJ9lH3OOCS8xAKv80iF/Xqrxiq5ewXK38XBbWQ7SUqMvjqvn98xyDyW1yrBQrzSlaNAenKaY6Ijx/FZGOTAC3XucLsu9uFMhm0w6WdnynZyH/m715CN9MSQJUeZ2bRjkoo5HkiosU2nJvgohK3x3JKOqspHkZ6QX951srmNcqtXsZ3fUcXa759GdvI934Dsge94B7L8LsedDHM8VcusZFxdpug6Ms37U6LOY549dhzZpKP/1+sUZ/f8HBO7L3Ef+VNTyEyM55vq5zzzUJwPowSs46GCZe63e4jH/FCE/bU6yHnBE+S9suxogxdepJA9UecY25rg+wXXWUG5FfoJjqIoiqIobYcucBRFURRFaTt0gaMoiqIoStuhCxxFURRFUdqOlpJxpVqXRLpZWjzmkEvXHcukWHQY2WqKQvG44/1CMR7WkkPq8xpu53fIyH5HBdnVLIWtWpDvN3mOIm6HLSIbcPzc+1qVEtd4hNstJ9gu/Y6Kjd152p2rPu4jFmHb5y1lr2iJ8nBHniLzqOOY7yzTHh6Mcb/eOisoV5Ks5BpxHPNRxFSq4kk1V9iMxijIZbO8plPBKWQDg+xzfWN8v/F1liL9zPyfIputUzpMR5h1bdBO7wlRdo+EKV4GvBxkq4MO6bzMvtTVYLuYTWZVh9g7ukcxtDLKKsMra44HDbzjyGodFP4X1liBejp8Glljgm1QrXFMJBxVu2WEwrNng+PkbA8rN3+O7/aGMzDYJx/6/m9uyryZB7Hd1fXryDY3eY6PPsI5t6ePlX3rW7wnLN9dRJZscN7s7KcUHAmyz/l9rCh/fphzVfE970G2cfUyslyW4z0cvh9ZiEWaxZfj+KxUeB43b91Cdv8k54CoYaXgydnHkFkvz7eR5YMLVcN7zPoW+3UgROH51u2XkMVijyLzhyiMt0I/wVEURVEUpe3QBY6iKIqiKG2HLnAURVEURWk7dIGjKIqiKErb0VIyDgR8MnaPLOz6SXnXm7CGr3s1lUlSkppxVHwdj1O6TTsqrwaMSxTjT8CfOHcO2UKeQpQMU26zGb5fvUxxsN5Hmerm3DKyxjgrYwZ8Xci2qmzV7h5WlNybZLXH/hWKZ7k9SsanZllZdMexXTTMtp9P8VrGHIJyxlICHXyTrLWDoaCcuKeS6dYW23vGcQ16e04iG+/ldT5zin3uhT95GtnE0hgyX5YDdGyG/aYQp8XYV6XIPDzMcbexwet3fIAi59wWpeB6nLKjZ5XC/9g65dyVGqXlqOOBhHVH/7Ieyokhh7g6M8o2zd6hyDxW5XhPW/bh88c4njLimET32A/6Ril4yxdYpfyNpiMUkjNnmsfEchf7/8lRVsGfDnK7YAdl2uwaRXTP1CSy+o6javGl28j2guzrcwWK95MTfcis5fXb3qIs3TXLa3pji319e5fzq3j5IEvccM4tO8T2wSFKweUS7xNDw8x6S6wgvpxaRDY+w/us2HlEc0/zntrRwQrF+SL7weY2Xzt7jNe8FW+Ou4qiKIqiKMprQBc4iqIoiqK0HbrAURRFURSl7dAFjqIoiqIobUdLybgmIhv3eE2FFCWpoKN6sEkzNA7zOOqoZJxKU1atpVh10fJX3KXu0JtXcpS9vDUKkN4RSnDbe47znaBQdpP+o/Q+yqqQj30fJano+Ht5LGVKcHtPfgHZsTVWMs6urCBLFq4hMxcpmV1ao0A33sN26d+lkFd3uJNVhxhXo5/pfO1RpCPoldMnepuy3V1KkceOPYxsIxhE5l1nf93Ks69vblMSt5bi5Yaf/auwSNm9PkjBtq+jjEyyHIvjXRSUl7e3kck6ZUIboRQZiU8gSyRYUXjMS2FdPBzHtWEeS9HPc6vl2BG93ZyPpqcp+3YLr+VgkjJyYmsT2YxDHk742FYeb8vp+chgjBG/v1kS9fmmsF1HiNcg4GM16eAUxezZDc7DJcfc8lKF/eG++/hAya5vBtlMhP2h4XiQJeuogj9Upoy8Xqc83NnluBf5OV9HorPIEhnuNzbK/lpv8Dx2d+eQdZ1mG6w7pO+pMc4p6QzHZ7XBz0smZniTvnLlBWTGy9d2FngsOwXee1uhn+AoiqIoitJ26AJHURRFUZS2Qxc4iqIoiqK0HbrAURRFURSl7WhpsQVFZNo0S4GsESoyFnOIpI6qnnbNYSM75KKGYzNXFhFWRV13/Lx9V5UiVm2YAth2FytAPvg9P4RsKE4xNNtNeWyqrxfZCv1RqY1SnFpfpjw88whlr7dPvB3Zp5a+hOyPf/YPkf3+IkWxnh7KctNT3IddXkBWd8jNVYc9XHUIxXVHReujiN/vl2ikud9NjFOSza5SAPbHWSU37CoI6mHo9VG8rJQd+zDsSz0dHOa9PlZKbVjKyJ5xDrzVAiuM9nVOIevcvcF9CPtX2lDYjcUoWSYTd5D5Mw5ROEyxsVh2VUamKO9PsOptX5VVms+Nn0GW9nJecE2wnlW2X9SwOnqp9fR8dDBGvJ7mebxjhoJorMTzWa5Qwu5yVLaemGZF7cRLV5F1bFIwr8yMI7sYp2SfXaU832hQHI8LjyW39Tyy1QbntOvXeb8LTlP2LezxAZAJP4Xs7DrHTtrxoE1HheNkZ4visc9/lu+3ymMORzhHLaf4fh2dfAAjGudrG0neO0oVjs/nV/lLAK3QT3AURVEURWk7dIGjKIqiKErboQscRVEURVHaDl3gKIqiKIrSdnz5Ssb3/DS8x7EkMg4p0lhKTR7HesqXpig5MkaJK+Wo4pjkoUjYUXUx1EGhrPquv41sfIqy12UfpcjsbYpsq1n+VHz/dW63m99CtrFJefLGHiXoD3z3B5C97REKZY9OPI7suce+G9mlv/oNZDs7iOQzecpt3zlMqbrPfxxZNUVRbDTKirTLKZe+fvSo1kRWV5vb3BuniBt3SHiJJQpyC8JxMuIQjyMRCsrXr7AvDZdYybU6TNEvEORYnDl+AtnuLkt0BzopQBaXCsj8jgcIbIaC7cT0aWT1cgWZWIqrEqYYPeaQlq87BOXYxBSy6gbfr7FFIXtpmVV5pyYos1brfKqgWOU1L5b4IES+6KgsfQSpVSuSyTb37QiLNcuKj6GpsWJ7ZZh95G7BUTm9ymt1/tx9yBYq7IdzeU50vn6K6HvznPtCEQrhHkdfD+S4XXcP7wmdg3xIIRLjPdBkKC2f6+JcuuflXFrj8zMiwurZtRqfABkddZWZ533HG+P47LnLhx56e9mmOxnezZMOwXu6zoctWqGf4CiKoiiK0nboAkdRFEVRlLZDFziKoiiKorQdusBRFEVRFKXtaCkZGxHx3SMTOXwtSdM3knqCUvCk4YZpxxqrQyg1UcsTGTasPLnbR2ltY5JVUZdfuols66nLyMKTFI8/dfcLfL8vULKctZTWRgYoPKfXryFbdFQ2/dA7vw9ZoZfn27FKsfEdD/0t7uP6p5E9/yIrSm50M/vzwiCy2BSF7PwtVjweyFAOHInyPCTBqtRvNNbnl/LIPbKkoyL0vMPLGx3heScdfX0p7xD9zrJ9gjemkOUWWYk3GuW4Gx3mtTJ1trcdoYhY8lBazllKoJ46hedIjOPd4+M0tJLk/BHxU1j0GvbDlTT7a8whbSaWed0eOEOZWwYpkfvyFI/n65Qsi8sUa/MDbJfdAh8q2NvjPo4i/oBfxieaq0cvLC1iu0aFkn2jQPvVN802861QOF11VD8/9sgxZHdf4Pya7riLLL9HYXf/LthMtc5xHHXIuVVHNf8HwuxfQ5MUZ5eEfWS9wP5Vj/MhgEqRc8Wc404fDfCXAGbE8cwAACAASURBVKTOO20mxTlc/GyX8ZEpZKkZXrfYvGOcjHM+6vJz3mo4hOxW6Cc4iqIoiqK0HbrAURRFURSl7dAFjqIoiqIobYcucBRFURRFaTtaSsb+Wk1iueaqiEt0i2TMQxmo5vhJ+YZDMKxlWMn47hKFraCHh+r1UVYK9lBWml+7imzrOuWswfMXkO2VHBU0G5S4vAEKgXsF7kM6BhD5+1gV+Jvf9y3IHn0vK82WqpS4hjspj33TA1PIlp/lPlYv/xay1BKlTe8Ur/lCjhVI7xujFLaWpODX63F0rCOIp9yQrjvNQrmxvdjOG2VfX6pS/o4bjpM1P8fEXi8rr37w0UeRfTLEKr6mRBnz6vO8Bt0lR4XWcUrxE9OU5xsFiogBP9tga5372O1nX5o8zgcDlhZYLXzUx/erezh/ZNIpZLEw237uLh8WmBw7hezm8hyy2fgZZHaAkvbybbZVqcx5wTiqNB9FatWaZLLNYz8Sj2O7RpkC6+I2ZdpAkvNIPsOq3VsV9ut8iNfv7HmKzPUa55vb86yebR33rMEBnkeHo1r+Xp19bm2LWSRDqXpigveYwBj7Q2eNVcX3BjkfxVZ5zCtLlOzPnvkGZPEo55RUlnNUOusQisMcxyudPI/tNPv/mZMUxvM7jntqC/QTHEVRFEVR2g5d4CiKoiiK0nboAkdRFEVRlLZDFziKoiiKorQdLSVj8fulEW8WoKI1CoYeS2ErayhUVlicVCrHKUmdDfMn70sl/pz6lqVM+1zJUQEyR8HqpEPEGq5QUEukWD11qMZ95Hv5fgNdm9zHxBSyd3/79yA7883fjmwsTqGy4hD3fJuU9Bb8vG6BU5S4ht/Htq89cZ1ZkZJl7ziryjb6YsjuO3Ua2fUrl5AdRawtS73eLImuGv6d4E8zmxgbR7YZYvXg7g3K88ODHCfJ/hKyD/+dDyH77GdZsXptl/ut9rCP3DfFa5pIUPicqFNEDLK7ymYHzy3QQ3Ew1KAAPNNFGb/oZ1+vR/las0Kx1+PlNSr0cxwn0pQnBx0VlK/OXUEWrbFK7ViU0utalsKsv5dj5yjiD4hE4833gO05Pphg6rxPxHoo2PaHeV2eybGPlHc5D3sspe5CeZT76KdQfMGx3708x9h2nX1uZZWdfXziMWS3Nlgt//o6b4wjXRRxizvcRzbEsXgqxn7jX2elbOt4KKQ8/ySyaox92IzyvmhyjvLtUkbS0cW5oruX79e7x4rpvR6VjBVFURRFeYujCxxFURRFUdoOXeAoiqIoitJ26AJHURRFUZS2o6VkXK1WJZVqFqBGqxQR7YTjJ8yj/Fn4aqdDROynZLZ4jPJrb40S0vw6haPGMAW+/PYGsk0fhTdrWD1yr7KI7OzQRWQf/gFWQX7HzBSyeJztUh6lLN1wCG9rW9vIZI1VYAt+VlV++i/+K7Lf/fd/gOz8hSlkpRHud6F0E9n7T59D9q1nWS3Zl+W53bj2ErKjSM2I5O5p8s4AxcbpwBSyUC8lweoar1U9OoJsM0PRdW6B1Y39IcqJ8an7kXV2URzc2OA4Gd7lmK30UVgMbj6DzIZ5bsPdFD5zVVYxTWRY8XVEKDGmsxzHkQGOiaSjonY2xQcXhh3zUb7ONvV0sJJrvM7qvf3jFKNzS3xt3wRfWyiwivpRpFozspprvpX4I5zTdjY5X5t5Vqf2eVgVuK+Tc0uqwXlkfZ33if7ZSWTVy9eQbQQ5jjsarIy/mqOInu9hXzeO8RR76AFkz196gq+1fKgg2sdK9usbnIdleRGRHeJ5rD9LufmpW3+J7GSa+50++W5k9z/yILJShRL0dcdcP+2YjzaDvL5DQxyfrdBPcBRFURRFaTt0gaMoiqIoStuhCxxFURRFUdoOXeAoiqIoitJ2tJSM/f6AxKLNslijxsq5VUv5r7yaQ7bZS9Hp8g4rHfbK/9/emQc5fpZ3/vvoaEl9qg9JfR/Tc9ieAztgD8ZsbE6HjdliqUAguwkkHIFsEthNstlQ1C7lxZtsKpBUlk3CQg4ge0AwIQlHYYwzxteMzdhz2HN1T7f6VLf6VKt1tK53/5DaqPv7tmYGj92y/HyqXOV59Dvf3/O+v7d/+vwecbVTn5+lq0Q7y4mZBAuQE2ARMRzhSstDK7zfhJerG//Wh19Psbe/7718LFGWzFI5bqvWda6quVHHlVJdhgU6V4FFse/c//cUO37/n1LMN2c5viZOia4QS3pdPSwRvnkvi2fBIFeVrWvgNvUkWawFuBrqbuP1eLB/mwS/ZMl1j4OrhLocLJgPWOTX1RhL3fkWrv68r4WlzUOd3I65wywJXvjusxS75TCve+zhixQbfB1XSvWusWD49IXHKNa1n/M/neExwBnilxlm2LNGl0Xan7FULe4NWipBuyzDn+VPvkyej881tI9ikRGuRtw5z9V2O1t43Fqe+RHFLsctLxVUI26B9G5tuIVLnP/hER7nggmu9m7cPF4P9HEV8MwBbsf46BjF9vRxvjrbeXxdt7xAMzDAY1/MUgG4283nMd/C4nE2xcJ6t4P7RGFygmI9vdx+CW4q5MLcBh2Dd1FsrIHvJ+kcVw9+yCJk98a5T2QyfHyrMa6Ynsnzyzx9r+HtuS2dcXmeXyCohD7BURRFURSl5tAJjqIoiqIoNYdOcBRFURRFqTl0gqMoiqIoSs1RUTKGMUB+qwA1O8Nybq6XRdLCPhYbf+ooVytcnOfqrrPPXKLYhJeFuxuOsCg2P8KVHTNxlodNA1dUbehjac0d5v0mkywORlZZUFtmbwr+FAeXLG3aWm+p+rnmpVhXD7f9LbdwVdSFC1xpebibxbjg0DDFOvazPNyc4/OIWqbL8SY+j3SIzyN0E7c9HubQbiOSh8e5td2GPVytttmS/+1pbjNjqfh98C2HKda/xmLj6Zs5D//5kbMUe4vnTootBFmAHOzjCuId589RLM9OJHwWYbHXzcNLpsAvKRQswr/p4wrK3eyKIjJlkbk9nF+uJc51ODlhxc2xbIilyDk+ZKTXWJ5fWObYhIfbPrVh6e8Oi0FahUjODZnfOuZ4vCx1e+sned1WHqvcFnN2LsFVuw/3s9i+OsN9bPUS53DHQe5j+92cwyuW6vGDDZybznmugpyK80s1iUbLmOvgnJs8d55i8y4WbN2W+4mrgStBT4xxxWjTxnn90613UGzZz21wapHvs8ctLxX42w5QbGOSq57nW/n+dOcN3FbNHdymldAnOIqiKIqi1Bw6wVEURVEUpebQCY6iKIqiKDWHTnAURVEURak5KkvGAJDfVnkxxzJtMsXS3Ds+/EaKrXQfpZj70W9QbL2FxSl/HVdZTa6zTLUe459n37jMcpvHyxJorJFFNl//Hor1DrJU6lji/XpTLBiOWoTimw7cQLHw1CzF2lpZvjs3ykJ2512/QLF7EixnTYOFz8UUV4JOGN6vP8sVeEOtvO7UdJhiLfWcQ/MpliyrEa+nCXuH79oSCx3l88llOyg2ZKnkjSBLxrBUBl9xcvu86tBrKPbI5AjFfjB6jGIHLMJug0UmdK9yJdLpZ3jdYA+L/C4PDy9py7l1dnFV7NkZlodnLPna18sVbsXMUKxg2W9vkiXLsXSaYvkJLqG80c5CastSM8UuLfK6/RZRcsLBFXO7XTwGAHx8u43bDXT1bP1bOZ3lax9f5fZeX+R8bTzE8nA6wsLpbAOLvd0hHsPnLNXyT5/iytHeQR6HPV4e1/tdgxRzWST7m5zcBrMOvmflb+d2KeT4Okej3Cf6Xns7xVZiPFb4oywoN8xZqkjv4/NYtQj6rQmWyP31XG29wcnCfz7AxyLjT1Ps28/yffb2u1iCroQ+wVEURVEUpebQCY6iKIqiKDWHTnAURVEURak5dIKjKIqiKErNUVEyzmSzmJ7dKsW2sTuJA+/5JYrdevevUuwpy8/RJ449QbHFNa666Opk0XV1guWx8Tj/3HsowNWS5y1y23qapabWHpb/HB6OjU/xz9u3JFkmrPPysYzG1yn2wD8fo9jCWRaxcqEcxe5+6wcpNryHq9R6LNJr83kWypIblynmaGXBL2PxhFuaWar7+kMPUOxihGXMasRd70Hfq7dW5PascGVTfyt3FNYfgcRMmGLHTzxKMdPH22uJsRAYS3OXXomNUaytniXB1TU+wmhLhmKtLZwjiQ5ezrXCMuHS/ALFOge5z7qEx4qeTjajF2fnKOaw/N3WEeJ1I2CB1BRY7A0FWIL2Jlh4nltg0bq3lyXy6HSUYk7D+xCLfG3Pol0mm4Nj23V1B1icra/n69zi5vFwIj9FscZ+FoDTY5zXaZflhQibOJvkyuk5w/eTbJwl2eMX+N5xOMgSu6+fx4WNMLdLo59zs++1fCwjy2coFl/jQbe9lcf10+MsVafTnEuHZvjXAfrcnNdDNx+iWEM9t3N4g188aVnnc8sJt198mdf94YOPUKwS+gRHURRFUZSaQyc4iqIoiqLUHDrBURRFURSl5tAJjqIoiqIoNUdFybjgciMZ2Co3NtWxiLtax1WBv3eChdhnYiwn3njTQYotHn+SYnNLfKhzTq5QjKilimkwRLGeoX0UiyW4IrNvaZlijihLgm0H+TzG18YpdrCXZbn/9Y37KfaDv/oixWbnWL7r7uuh2LkzH6PYh/7dH1Jsv5+Fsu4uFkOfPMltMDMZplhHM0tmX3vwexQ7883HKdbWx5JZNeLIC+q2ScU2oXh1hSsA+1tZMFzYYMF89ARL2M8+ME+xwQN87cemL1KsvoFlx4m0RSbPWKpxh1iyDFv2UTAs3s/GWBSus1Sf9QiLoQMWyRiGBWBXgceFUDsLpBfXLlAsM839PZfi/Ub8vN/mSZa0Dx1guTP+LI8Vrn6WhyPTXOG8DkGKVStmW7M5YLnOA1wp2+FhyXh9nWXkPS6+Lt7hYYqlk/yCig+8j8OWvpi1CMrPHH+KYitcUBjnh7gvNq/yWFpIcn9P13GO9PVzf4p7uU2zYyw8R09aJO0WblP0WMR7iwg+UMeS8bKXx4X5cZa+19Z43VQr5/WQl+8xnZaXCmJrPG5VQp/gKIqiKIpSc+gER1EURVGUmkMnOIqiKIqi1Bw6wVEURVEUpeaoKBmbbBaZbZWMF4Is/kzNnaLYP/3lDyh25KdeRbF/8TMcW/awFNnjZwEsP8aVGGNgecyxxPLroZ++jWIDe1kS9Pl4XaelOunUFItdbjdLYWdOs7D4vS98k2KNXp579oRYlk41cSVLGeXL+g//448p9pF3vpdiJ+MsXi6tslXXFUxT7Ct//VWKnTnJorUHXPXWt2yR4KoQp9OF1u1SsaV9MgWWItM5brN0hkX5QpDlRE+aK0K7hAW+9VVuW08d96eRaa4onBOuWD23zLk+0MmS4JPjXJ10I85CYE/HAYqlRrnvFJxcobtztItiDe08LsTXuaq4LHIb7PVYqnEH+DymYgHeXhtfX88Ct2ldBwvPT09x9eWubu7bkQgvV41kDDCd39oeZppf9ujv7qPYaYusmjzLwul0gNs7cIhF707PEYrNWMZmh5OvVUc35/WeHj7m1Sjf7/JZftGgU7gfT2W5YntjfT/FLsTOU8zh4POtj/EY8PA8/zpAfYpfbunq5jHF6eA+MTPL9yyfjwXvLsvLAr5OfnkkEuHrO5rkqsXxRs7/VIqXq4Q+wVEURVEUpebQCY6iKIqiKDWHTnAURVEURak5dIKjKIqiKErNUVkyBrBd7WoNsUz12FNh3rDD8vPncRZi65a4yqTffyPFzp07TbFlFwuV3X0sq8bSLAmup1hGHk1wlebbNriJgkGW0fJ5jvX1smT2o+M/pFh97DmKNedYAIsnWKp2ewoUm4uz7LU8z5VSzz/LslznrXfwfpO83+kTXM02vsrnMQiujhto3EOxtNdSoTLBod3GZDNIz4e3xBYswuJ4mIXi48dPUKzJx5Jg4yr3sfZmzodmD+fwYIj7zsXHuJp0boBz02mpWBqP8nlEc9y3A0HuY5fmubrrs8+y8NnWy/2ux8f97lyMK+E2b/CLAYODLDIP1nP+T2dYWEyHeUyBm4Vn5yQfX6Ge163fs5diTTEeB41lvLTFqhM3jNkqgAcCWVpqeoav/XBLM8XmwOJ9IMiye8Hy9/niEo8jQz6utG+2l14GkBi3VAH38th3y0HunxHbva2Lc9Pj5mNeWbBUuw5wtevWLO/j8so5ijmcnJv79/F90e/i5XJZbhcT4HtbKMQv3yRGeHveFb4eAwNcpfnMmTMU6wTn/0KcK0FXQp/gKIqiKIpSc+gER1EURVGUmkMnOIqiKIqi1Bw6wVEURVEUpeaoKBk7XU40tW2tnhgd4Z+jLwyxEPW6o1wpOG+pqGq6eF2fb57XDbL81LvO22tpY9nRZ1hkNsZSJTHBx9J78CDFHPM8L0ynWVgcTbLse2qez23BUvG4Zz+LcbFZlq6amywm7jSLZ/HzLACPBriKNPq5Aml3N+/32PfCFMulWKrzD7JE6F3hNmgNccXQM4sW8XiXWUms4xsntlYKPfUM51K6jeXcQoGFcNdFFi9DbSwxRi2C+eQ479fpYFG479DrKfb4IguV2SRLjK0bLIk3dQzyPhpZxI07uSrwUgO3S4+LZemlZZaChwY5R5L1LCjPL1okXuExYGiIJeh8K6/77KN8LJ05Ho+S6yyCL4S5DVoPsgg+f8oiWVr6HcY4tPtkUChsHTemCxZZu4PbDHm+foM+i1A8x9Wps+28vXyab2lj4HV7wOJsPMoVdmfWuLp3qI8rau/bw2PfZJqrFjuFr2lnJ/f3fI4l7cVL3Gen8zymDO9lsT1qEYrjlgrPyST3T4fwui0tfL6FEJ+bM8kvM8RHeQ4xNDREsVnDFZQHfXzPr4Q+wVEURVEUpebQCY6iKIqiKDWHTnAURVEURak5dIKjKIqiKErNUVEyTubyOB1d2hLLiOXn7dssVSGnWRAqeLiCYUfaImOy64u2MZauzCqLly3NLGwdvoOr83a0stg4F+FjXvXy+UYiJynmK7BgtZxiISo/w1JYd18/xaILLEW2Hfbzcqe5im7TIIt7ybPcVhPjj1CsIXKYYuujLOn9KMeC2uIsX8u1Lm6XpXWuqjmc4nWrkbW1GL7/3e9siTmcLKs6l/hvh2wH53BaWEQszLLYmHKxeGmriup1c944cyz6HRnknPMssRS/EuBrb+J8Hokw59dAAx+fv5GPb87N+/XU8XJuS5XaZmGh0n2IhcW19SWKhdNcGXlPA+/3cA+/uBCf4HVNlivwrsdZqL/ReYhi6Tbu2/kci5wA73e3MS7AbBuzQ9kQLTc9M06xsfQIxda9/MLGunBbHDY8hjs6ebyezLCZnSxwRdyHn+H8uvQtHueOvoFfChnoaaNYPs/3olw6TLENywsEqbEUxUYTfMxd3d28nEXibU3wyx4bLhbgAwHOdYfw+doqD5sCv2gQCPA1go9fXJiPcD9JrXOfiPu5XSqhT3AURVEURak5dIKjKIqiKErNoRMcRVEURVFqDp3gKIqiKIpSc1SUjAsAtutPXb0sVK6Mc9XKwGE2hafXFimWTLBc2rjB2+v2cpXQ5ldzhdZ9N3CV0OY7uRpxbydLcDc7bqbYYP0gxTxLfB4mxLJXu2F5cmGGpdIn/46319HGcm5rD59HIMTtfP4My9KxJsvPzDt4OddzLI81t7DY5YqyLI0WlswSGcscOsDS62WLZFaNuAoFBFJbJciNDFeJnsxwDhemuc2GbLJvjNvC5+Rquj1BrsY6m2JBudHJgqZj0VK1dZKPubGehcCpOpaMV3JcQXmowXZuLJUGG1h2XDb84oKx9KfeXhYg65ZYvF+u56rPoxFe7kKaxcZBD+/X18hCaqGeQugotFJsZOQSxQaGhyl2Jsp5VZVkAWxLz5kNlnPzec6bTDu3d1sdjxkL85xf07MswPtcvD1XB1+/6WSYlytYrrOwnN4bfB3F2tMsGbviPK5PxlkSTzTySyGXVjlH/Jbqwc+tcB52dLDc3G6pIi0OFoAvj/L44fFwYieTfE9INvPLI8trLFDf6OdxwenkCvrD9bzfacuLO5XQJziKoiiKotQcOsFRFEVRFKXm0AmOoiiKoig1h05wFEVRFEWpOSpKxhA34N4mMjpZfu2Pszi46uVNDzWzKNa+xpLUYh0LgRGLdOheT1Ds0up5ijU/yMLbrG8P79fFwqJ79dsU6wELxYUcH988WFqrX4zyulmW7+rc3H7rDz9MMWcPC7vRBZZU2zpY5FyNcfs99djXeN12FiU3UlxttN3J8+Uli/DZFGJR3e/g82DtbPdJ5XJ4bnGrPNjVaalO7eeKuJ46ruS9vMS57hCWtYMplsTjZ7mFXGtxis3HeV23m9vb18bXucnL8ms+weJxuoXlv0iS+0Qgy22VG2eBumuI2+ryDPcdxxMsyvvaOK83DEuR6TT3O8nySwCjfBpw5znYucFyZ0QsWbzOfTuV4P7UEOcqzdXZKwCYrfeFkEWAz2T5uqyM8Hg9sc73CVsf87cNUqy9mcfm6SmuZBzw8fVrGODx6/C7/zXvw8l5s/zcWYqNxPne1pnhSsFLZ3mMXGvlNogmuf0WFnnd9Dq/kJNI2aRvbtNgnq9bdINl6ZSfKyPnMnyPsfxQAdLjHBw6wNXHTYTHhT1tLHNXQp/gKIqiKIpSc+gER1EURVGUmkMnOIqiKIqi1Bw6wVEURVEUpeaoKBm7RdDj2ipLrqywYNjc3UcxmWRp7uwyC3yzUxcpNhdlSTYRY1FyzMlCWTP7ZFiLspwILgCMdmHxcinKTRQUlmTFsHwNwwKko4sls+xcmGJ7jr6a9zHHQtniMlfa7OmyVDe+wG3amOPKmPVenvNGE3zdwAU5AbCkamMmyqJky14W48Du+q5TV+dGb//WvKub57Zdtvzp4CiMUyyQ48qrmQ2WGBdiYV4uztKhZFhQdnWy7B6zVOwd7uGDTs+weL/PIhSvLfO4EGzka5oNW6pid7HwWZhmYTEofMxnR09SrL+dhcWVRq6oOtjXS7G5ac7NTIKPOdVgqcAb5+vmmGeRM9TNY8DoeU52916bZFx9uF0uhAJbq+JevMj5kM1NUqyzo4tiS0unKJYc47F5I83tONXGuQ7hvJ6JcF4Hg/spFstyPnhSLLpeinG/869zZd+04TEga+mzrWkem8+t8aCbSnNutntYdg90sBQMy0swIvzSQ5fl3jayzu2Sn+bxv3svjxUOJ9+PZyM8lgWDfH3HRni5SugTHEVRFEVRag6d4CiKoiiKUnPoBEdRFEVRlJpDJziKoiiKotQcFSVj8TjhHt4qSg24+CfWx+dZfg1HWcQSP1dEnJplmbDOM0ixHJYp1t3E4tTsDAux3cNXK+uxTCX9LEC6ZixSZIBFsbkcC2DIhinU1s5t9eBzZyh2oImXG+69hWKTk4/w8VmktTVkKOa3TXm5IKeV9gBLa0uLIYoNDNdTbG6Br281ks8KYtvEcxNgGS53mSX7VJxF10jqBMU6c3xdEOR2jCx4Kea0yPNiqcTbEuJ+Nz/N+Z+yVM4NLvEY0BjiWHrxAsVcft7H1AgfYNYxQbGegX6K9Tdzv5uJXKLY0BCLxzMnRikmBZaCTRtfj05Ltd2pSa68OtjDx5yzVHNuTbKgGbnAVamrkfxGHmsXt+b2ygoPGgb8okirpXp20FIlerWe100mWPTu3sd90TnHudTUw4L55DgLrDljqVpsqQrfZhlLxxbDFOvv5QrnqSRvL7HOb8t4llnO7V9k+ToU4tv6qKUct9vNovWQl8cUscjInfWWgYa7GGIxFqP9ddwG8TjnekNDA8UyGRbVK6FPcBRFURRFqTl0gqMoiqIoSs2hExxFURRFUWoOneAoiqIoilJzVJSMM9kswtsE4oEerlqc7uHNeFf5Z9f9FtlXPBbhKGGR0XpYkgqPc1XIvhteRbEVHwuBTkt1Y6ew/NcWYmmtpY3FM5NjuTme4YqS+QL/VHxsggXggI/FsxXDItbTZ1hGnopZJFVLgc8OS9svzrMI3hHka2nbntPJwpujjrdX52FZumDZXjXilBwaHVtzZ+w4S60dzVzFNxYJU2ygmyu5rrg4N3vAbesC52tBWOLNW6RNM8E5ErqBpc2JeZaMly3H1xBjOTE6xcfSF2Q50ZljCdQ1YBlTDCfJXJb34V7l83Uscn9qn2HpO8qXA/2rLKROTU9RLJjjfrKR5bEiwoWvsVHgquft7SyWVyO5XA7Rpa33icVVrmTs9bF4nzecw94hzqXLj/GLLOt+rrqbbebteeos47ql8Ly7n8fDrhRL4o+NcH+feYJzuLODnx+cPsPjofRy315L832iLsd53d7EknY4wznXGeJc8nn5vhi1VEtubeW+4zO8rnuRz63Bst+1jTDF/G5u57kIS/t79+6hWCX0CY6iKIqiKDWHTnAURVEURak5dIKjKIqiKErNoRMcRVEURVFqjoqSsREHcp6tP3ceXmQBzHUTi72hIwMUq8/zfCprm2LVs4zmc7MAZpz8U+xLy1wR96BhiWs21E2x/t4eii3MsEwY6LZUO80foJjfIoplLNUy00GuyDxTYMEqaCwGZIaPZSDB1R43MiwoOxy8vV4fi6aGXUzMFFiq7ndyOu0dYtk2OscymsX5q0oyGxuIhLcKhfUOzq98kqtYOyzyn4cvHzo3WGyfCHNl01Af53B+lkXcqZylGvFeFv7nLFXAOwJcoXjVIpDOT3NHzlvOYzLMcrMYy9Xf4ArFkRy3n3OW1y0U+FikwLk5a1muO8cS9Ow8y6xdQe47ExbxODDB2/P38jUyET6PnEXSrkaSuRzOLC5tia14l2i5+iWubpw8xeNhbz/nTTrJL57MrnNe9xzhl2AONPM9IW3pn6vjlyk2tsA5Ej7HQjGm+No/fG6EYg379lOseYlF4Q3LjbG/0VK1e5VzKdjOfXZqigXl5ibO675+KBHxngAACftJREFUW+Vt7ndzc3x/ak7wfSy3eI5iy5ZqyTfdxNXtk0meBxQsMn4l9AmOoiiKoig1h05wFEVRFEWpOXSCoyiKoihKzaETHEVRFEVRag4xNsFv80ORBQAWo0pRXhIGjDFsHO4i2ieUXUb7hKJsZcc+UXGCoyiKoiiK8nJEv6JSFEVRFKXm0AmOoiiKoig1h05wFEVRFEWpOXSCoyiKoihKzaETHEVRFEVRao6an+CIiBER/uGdnZd/TERueTGP6cVGRN4vIo9W+Py7IvK+q1l223qfEZGPXq/jVKoDEfkbEfn0S7i/u0Xkm2X/vkNERkRkXUTe8SLs70kROXi9t6vUPrvdN67jdo+IyOPXe7vVzq5PcEQkLCJv3u3jAAAReTuAuDHmmeuwraueOLzUGGPeZoz50k+w6h8B+ISI8C+fKsrVcx+APyj7970APmeMaTTGXPfBHcW8vfdF2K6iXG+2942fiO1/2BtjzgBYLd3jXjHs+gTnSohIxV88v858BMBXXsL9vawwxkQAXADwr3b7WJTqx9Z3ReRWAC3GmONl4QEAz+2wDRGRFzpO/SOAN4gI/7S3ouwC19A3rif/G8Cvvkjbrkp2dYIjIl8B0A/gn0qPp/+jiAyWZp8fEJFJAA+JyF0iMr1t3eef/IiIU0Q+ISKXRSQuIidFpM+yv9eLyJSI3GX5rA7AGwE8XBa7TUSeEJFVEYmIyOc2n16UHaerbPljIvJBEbkRwF8AuL10Xqulz1tE5MsisiAiEyLyyc3Bu/TE5zER+ePS/sZE5HWl+JSIRDe/VrrStn68iHxORGIickFE3rT9OHe4JjeIyPdFZFlELorIu7ctcgzAz9rWVV4eiMgtIvJ0qa98FYB32+f3iMipUh4+LiJHyj7rFpH7S3k3LiK/WfbZp0Tk6yLytyKyBuD9lt2/DVv72GUAe/DjMcBTys/7ROQxAEkAe0r7/cdSXo6KyIfKtuETkS+JyIqInC+NI8+PF8aYNICTAO5+gU2n1DjV1DdK6x0sG4/nReQTpXile9MPS6ufLvWpny/9+xiAN4mI5wU10ssJY8yu/gcgDODNZf8eBGAAfBlAAwAfgLsATO+0HoDfAXAWwAEAAuBVANpLnxkAewH8DIApALftcBwHASS2xV4N4LUAXKXjOg/g49uO01W2/DEAHyz9//sBPLpte18G8A8AmkrrXwLwgbLlcwB+GYATwKcBTAL4nwA8AN4KIA6g8Rq29e8BuAH8PIAYgLZKx1lq76nSMbgA3AJgEcBNZefwTgBP73be6H8/cX+rQ7Gs/mZu/ByALIBPlz6/BUAUwNFSHr6v1Nc8KP5BdBLAfy5tZw+AMQB3l9b9VGlb7ygt67Ps/+8A/M62WBhbx4Bjpdw/WMpDN4AfAvgzFG84NwNYAPDG0vJ/gOKNoRVAL4Az4PHiTwF8drfbX/+r3v+qrW+gOLZHAPxWKe+bABwtfbbjvan0uQGw17KPNQBHdrutX6r/qvkrqk8ZYxLGmNRVLPtBAJ80xlw0RU4bY5bKPn8XgM8DeJsx5skdtuFHcQLxPMaYk8aY48aYnDEmXNrGndd+KsWnTADeA+D3jDHx0vY+A+AXyxYbN8b8tTEmD+CrAPoA3GuM2TDGPAAgA2DvVW4rCuBPjDFZY8xXAVzElZ+83AMgXDqGnCm6SPej2H6bxFFsK+XlyWtRHLw3c+PrAJ4q+/zDAD5vjDlhjMmboqu1UVrvVgABY8y9xpiMMWYMwBdQzMVNnjDGfNMYU9ih71I/24G/McY8Z4zJAegEcAeA3zXGpI0xpwB8EcAvlZZ9N4D/ZoxZMcZMoziZ2Y7mrXIlqq1v3ANgzhjzmVLex40xJ4AXdG96RfWDl9JvuVamrmHZPgCXK3z+cQBfNsY8W2GZFRRnyM8jIvsBfBbAawDUo9heJ6/huMrpQLHzlP8o3QSAnrJ/z5f9fwoAjDHbY41Xua0ZU5qyl33efYVjHABwdPMrtRIubPWSmgCsQnm50g17bmwyAOB9IvIbZbG60np5AN3b8sMJ4JGyf1+p31I/24Hy7XQDWDbGlA/+Eyj2y83Py5e3HYPmrXIlqq1v7HhfewH3pldUP6iGJzg7/dpneTyB4kUE8PzTkPJfD50CMFxhH+8C8A4R+ViFZUaLm5byScKfoyjV7jPGNAP4BIpfgW0eE8qPC8W/NG3HDxS/6smi2Ek26QcwU+GYduJqttUjIrLt89krbHcKwMPGGH/Zf43GmPJXw28EcPonOGalOojAnhubTAG4b1sO1Btj/m/ps/FtnzUZY/5l2fpX+vXeMwD2X8Vxlm9nFkCbiJQP/uX5HkHxq6lNyL+D5q1yZaqtb0yh+FWXjUr3Jiule1sdik/zXxFUwwRnHjtfxE0uAfCKyM+KiBvAJ1H83nOTLwL4ryKyT4ocEZH2ss9nAbwJwMdkhzouxpgMgAex9TFfE4rfWa6LyA0APlq2/AKKA+y/laLk/CvYOsmaB9C7KX6Vvnb6GoD7RKRJRAYA/AcAf3uFc7cd69VsKwjgN0XELSLvQnGA/84VNv0tAPtF5BdL67lF5FYpStOb3Angu9d6zErV8ASKftZmbrwTwG1ln38BwEdE5GipLzWU+l0TgCcBxEXkd0tir1NEDknx7Y+r5Tu4xq95jTFTAB4H8Psi4i2JnR/Aj/P9awB+T0RaS4P4r5evLyJeFJ2F71/LfpVXHNXWN74FoEtEPi5F+b5JRI6WPtvx3lTCdl+9E8BDxpiNazimlzXVMMH5fQCfLNngv21bwBgTA/BrKE5kZlB8elL+VtVnURzkHkDxov8linJy+TYmUZzk/CfZ4Q0iFL/HLPdYfhvAL6D4veUXUPRiyvkQioLzEopCZHkhpYdQfPV1TkQWS7HfKB37GIBHAfwfAH+1w7FciStt6wSAfSg+7bkPwM9t85KI0lcAb0Xxe+NZAHMA/jtKk0kR6QJwE4AXo1aJ8hJQmsi/E0W5fBlFAf0bZZ//CMW8/hyKj8xHS8tuTqzvQVHyHUcxt74IoOUa9v80gFjZQH21vBdFmXIWwN8D+C/GmAdLn92L4ngwjuIfKV9H0Y3Y5O0AjhljrvQEU3kFU219ozQevwXF/J0DMALgDaXFr3Rv+hSAL5Xuq5tvwv4bFN/ufcUgW79uVKT4auqvm+tQ7K/WEJHPALhsjPmz3T4W5eWLiLwVwK8ZY6571eLS9j8K4D3GmDtL/z6B4huGlRw8Rdl1Xqy+UXrq+XljzO3Xc7vVjk5wFEV5WVN6srgHxa8Y9gH4NoqVkf9kVw9MUZRdpZrfolIURbka6lD8enkIxTdE/h+KNXMURXkFo09wFEVRFEWpOapBMlYURVEURbmu6ARHURRFUZSaQyc4iqIoiqLUHDrBURRFURSl5tAJjqIoiqIoNcf/B2HhMepKb7+4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "for i in range(0, 9):\n",
    "    pred_label, true_label = class_descr[x_test_adv_pred[i]], class_descr[np.argmax(y_test[i])]\n",
    "    plt.subplot(330 + 1 + i)\n",
    "    fig=plt.imshow(x_test_adv[i])\n",
    "    fig.axes.get_xaxis().set_visible(False)\n",
    "    fig.axes.get_yaxis().set_visible(False)\n",
    "    fig.axes.text(0.5, -0.1, pred_label + \" (\" + true_label + \")\", fontsize=12, transform=fig.axes.transAxes, \n",
    "                  horizontalalignment='center')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"train_detector\"></a>\n",
    "## 3. Training the detector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load the detector model (which also uses a ResNet architecture):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = get_file('BID_eps=0.05.h5',extract=False, path=ART_DATA_PATH,\n",
    "                url='https://www.dropbox.com/s/cbyfk65497wwbtn/BID_eps%3D0.05.h5?dl=1')\n",
    "detector_model = load_model(path)\n",
    "detector_classifier = KerasClassifier(clip_values=(-0.5, 0.5), model=detector_model, use_logits=False)\n",
    "detector = BinaryInputDetector(detector_classifier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 32, 32, 3)    0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 32, 32, 16)   448         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 32, 32, 16)   64          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 32, 32, 16)   0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 32, 32, 16)   2320        activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 32, 32, 16)   64          conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 32, 32, 16)   0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 32, 32, 16)   2320        activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 32, 32, 16)   0           activation_1[0][0]               \n",
      "                                                                 conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 32, 32, 16)   64          add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 32, 32, 16)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 32, 32, 16)   2320        activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 32, 32, 16)   64          conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 32, 32, 16)   0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 32, 32, 16)   2320        activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 32, 32, 16)   0           add_1[0][0]                      \n",
      "                                                                 conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 32, 32, 16)   64          add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 32, 32, 16)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 32, 32, 16)   2320        activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 32, 32, 16)   64          conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 32, 32, 16)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 32, 32, 16)   2320        activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 32, 32, 16)   0           add_2[0][0]                      \n",
      "                                                                 conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 32, 32, 16)   64          add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 32, 32, 16)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 32, 32, 16)   2320        activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 32, 32, 16)   64          conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 32, 32, 16)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 32, 32, 16)   2320        activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 32, 32, 16)   0           add_3[0][0]                      \n",
      "                                                                 conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 32, 32, 16)   64          add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 32, 32, 16)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 32, 32, 16)   2320        activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 32, 32, 16)   64          conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 32, 32, 16)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 32, 32, 16)   2320        activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 32, 32, 16)   0           add_4[0][0]                      \n",
      "                                                                 conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 32, 32, 16)   64          add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 32, 32, 16)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 16, 16, 32)   4640        activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 16, 16, 32)   128         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 16, 16, 32)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 16, 16, 32)   544         add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 16, 16, 32)   9248        activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 16, 16, 32)   0           conv2d_14[0][0]                  \n",
      "                                                                 conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 16, 16, 32)   128         add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 16, 16, 32)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 16, 16, 32)   9248        activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 16, 16, 32)   128         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 16, 16, 32)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 16, 16, 32)   9248        activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 16, 16, 32)   0           add_6[0][0]                      \n",
      "                                                                 conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 16, 16, 32)   128         add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 16, 16, 32)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 16, 16, 32)   9248        activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 16, 16, 32)   128         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 16, 16, 32)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 16, 16, 32)   9248        activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 16, 16, 32)   0           add_7[0][0]                      \n",
      "                                                                 conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 16, 16, 32)   128         add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 16, 16, 32)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 16, 16, 32)   9248        activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 16, 16, 32)   128         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 16, 16, 32)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 16, 16, 32)   9248        activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 16, 16, 32)   0           add_8[0][0]                      \n",
      "                                                                 conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 16, 16, 32)   128         add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 16, 16, 32)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 16, 16, 32)   9248        activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 16, 16, 32)   128         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 16, 16, 32)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 16, 16, 32)   9248        activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 16, 16, 32)   0           add_9[0][0]                      \n",
      "                                                                 conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 16, 16, 32)   128         add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 16, 16, 32)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 8, 8, 64)     18496       activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 8, 8, 64)     256         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 8, 8, 64)     0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 8, 8, 64)     2112        add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 8, 8, 64)     36928       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 8, 8, 64)     0           conv2d_25[0][0]                  \n",
      "                                                                 conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 8, 8, 64)     256         add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 8, 8, 64)     0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 8, 8, 64)     36928       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 8, 8, 64)     256         conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 8, 8, 64)     0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 8, 8, 64)     36928       activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 8, 8, 64)     0           add_11[0][0]                     \n",
      "                                                                 conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 8, 8, 64)     256         add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 8, 8, 64)     0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 8, 8, 64)     36928       activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 8, 8, 64)     256         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 8, 8, 64)     0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 8, 8, 64)     36928       activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 8, 8, 64)     0           add_12[0][0]                     \n",
      "                                                                 conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 8, 8, 64)     256         add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 8, 8, 64)     0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 8, 8, 64)     36928       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 8, 8, 64)     256         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 8, 8, 64)     0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 8, 8, 64)     36928       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 8, 8, 64)     0           add_13[0][0]                     \n",
      "                                                                 conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 8, 8, 64)     256         add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 8, 8, 64)     0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 8, 8, 64)     36928       activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 8, 8, 64)     256         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 8, 8, 64)     0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 8, 8, 64)     36928       activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 8, 8, 64)     0           add_14[0][0]                     \n",
      "                                                                 conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 8, 8, 64)     256         add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 8, 8, 64)     0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 8, 8, 64)     0           activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 1, 1, 64)     0           dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 64)           0           average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "classifier (Dense)              (None, 2)            130         flatten_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 469,698\n",
      "Trainable params: 467,426\n",
      "Non-trainable params: 2,272\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "detector_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train the detector:\n",
    "- we expand our training set with adversarial samples\n",
    "- we label the data with 0 (original) and 1 (adversarial)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_adv = attacker.generate(x_train)\n",
    "nb_train = x_train.shape[0]\n",
    "\n",
    "x_train_detector = np.concatenate((x_train, x_train_adv), axis=0)\n",
    "y_train_detector = np.concatenate((np.array([[1,0]]*nb_train), np.array([[0,1]]*nb_train)), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform the training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "10/10 [==============================] - 11s 1s/step - loss: 0.0214 - acc: 0.9950\n",
      "Epoch 2/20\n",
      "10/10 [==============================] - 4s 447ms/step - loss: 0.0058 - acc: 1.0000\n",
      "Epoch 3/20\n",
      "10/10 [==============================] - 4s 426ms/step - loss: 0.0061 - acc: 1.0000\n",
      "Epoch 4/20\n",
      "10/10 [==============================] - 5s 453ms/step - loss: 0.0139 - acc: 0.9950\n",
      "Epoch 5/20\n",
      "10/10 [==============================] - 5s 478ms/step - loss: 0.0064 - acc: 1.0000\n",
      "Epoch 6/20\n",
      "10/10 [==============================] - 4s 426ms/step - loss: 0.0059 - acc: 1.0000\n",
      "Epoch 7/20\n",
      "10/10 [==============================] - 5s 518ms/step - loss: 0.0056 - acc: 1.0000\n",
      "Epoch 8/20\n",
      "10/10 [==============================] - 5s 490ms/step - loss: 0.0053 - acc: 1.0000\n",
      "Epoch 9/20\n",
      "10/10 [==============================] - 5s 543ms/step - loss: 0.0081 - acc: 1.0000\n",
      "Epoch 10/20\n",
      "10/10 [==============================] - 5s 520ms/step - loss: 0.0056 - acc: 1.0000\n",
      "Epoch 11/20\n",
      "10/10 [==============================] - 5s 497ms/step - loss: 0.0050 - acc: 1.0000\n",
      "Epoch 12/20\n",
      "10/10 [==============================] - 6s 552ms/step - loss: 0.0049 - acc: 1.0000\n",
      "Epoch 13/20\n",
      "10/10 [==============================] - 6s 630ms/step - loss: 0.0054 - acc: 1.0000\n",
      "Epoch 14/20\n",
      "10/10 [==============================] - 6s 599ms/step - loss: 0.0046 - acc: 1.0000\n",
      "Epoch 15/20\n",
      "10/10 [==============================] - 6s 633ms/step - loss: 0.0047 - acc: 1.0000\n",
      "Epoch 16/20\n",
      "10/10 [==============================] - 7s 657ms/step - loss: 0.0042 - acc: 1.0000\n",
      "Epoch 17/20\n",
      "10/10 [==============================] - 6s 608ms/step - loss: 0.0040 - acc: 1.0000\n",
      "Epoch 18/20\n",
      "10/10 [==============================] - 6s 635ms/step - loss: 0.0037 - acc: 1.0000\n",
      "Epoch 19/20\n",
      "10/10 [==============================] - 7s 673ms/step - loss: 0.0035 - acc: 1.0000\n",
      "Epoch 20/20\n",
      "10/10 [==============================] - 6s 610ms/step - loss: 0.0031 - acc: 1.0000\n"
     ]
    }
   ],
   "source": [
    "detector.fit(x_train_detector, y_train_detector, nb_epochs=20, batch_size=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"detector\"></a>\n",
    "## 4. Evaluating the detector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the detector to the adversarial test data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial test data (first 100 images):\n",
      "Flagged: 100\n",
      "Not flagged: 0\n"
     ]
    }
   ],
   "source": [
    "flag_adv = np.sum(np.argmax(detector.predict(x_test_adv), axis=1) == 1)\n",
    "\n",
    "print(\"Adversarial test data (first 100 images):\")\n",
    "print(\"Flagged: {}\".format(flag_adv))\n",
    "print(\"Not flagged: {}\".format(100 - flag_adv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Apply the detector to the first 100 original test images:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original test data (first 100 images):\n",
      "Flagged: 100\n",
      "Not flagged: 0\n"
     ]
    }
   ],
   "source": [
    "flag_original = np.sum(np.argmax(detector.predict(x_test[:100]), axis=1) == 1)\n",
    "\n",
    "print(\"Original test data (first 100 images):\")\n",
    "print(\"Flagged: {}\".format(flag_original))\n",
    "print(\"Not flagged: {}\".format(100 - flag_original))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the detector for different attack strengths `eps`\n",
    "(**Note**: for the training of detector, `eps=0.05` was used)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "eps_range = [0.01, 0.02, 0.03, 0.04, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
    "nb_flag_adv = []\n",
    "nb_missclass = []\n",
    "\n",
    "for eps in eps_range:\n",
    "    attacker.set_params(**{'eps': eps})\n",
    "    x_test_adv = attacker.generate(x_test[:100])\n",
    "    nb_flag_adv += [np.sum(np.argmax(detector.predict(x_test_adv), axis=1) == 1)]\n",
    "    nb_missclass += [np.sum(np.argmax(classifier.predict(x_test_adv), axis=1) != np.argmax(y_test[:100], axis=1))]\n",
    "    \n",
    "eps_range = [0] + eps_range\n",
    "nb_flag_adv = [flag_original] + nb_flag_adv\n",
    "nb_missclass = [2] + nb_missclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(np.array(eps_range)[:8], np.array(nb_flag_adv)[:8], 'b--', label='Detector flags')\n",
    "ax.plot(np.array(eps_range)[:8], np.array(nb_missclass)[:8], 'r--', label='Classifier errors')\n",
    "\n",
    "legend = ax.legend(loc='center right', shadow=True, fontsize='large')\n",
    "legend.get_frame().set_facecolor('#00FFCC')\n",
    "\n",
    "plt.xlabel('Attack strength (eps)')\n",
    "plt.ylabel('Per 100 adversarial samples')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv36",
   "language": "python",
   "name": "venv36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
